Research ArticleIntelligent Bar Chart Plagiarism Detection in Documents
Mohammed Mumtaz Al-Dabbagh12 Naomie Salim1
Amjad Rehman3 Mohammed Hazim Alkawaz12 Tanzila Saba4
Mznah Al-Rodhaan5 and Abdullah Al-Dhelaan5
1 Faculty of Computing Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia2 Faculty of Computer Sciences and Mathematics University of Mosul Mosul Iraq3MIS Department CBA Salman Bin Abdulaziz University Alkharj Saudi Arabia4College of Computer and Information Sciences (CCIS) Prince Sultan University Riyadh Saudi Arabia5 Computer Science Department College of Computer amp Information Sciences King Saud University Riyadh Saudi Arabia
Correspondence should be addressed to Amjad Rehman arkhansauedusa
Received 30 March 2014 Revised 21 June 2014 Accepted 7 July 2014 Published 17 September 2014
Academic Editor Iftikhar Ahmad
Copyright copy 2014 Mohammed Mumtaz Al-Dabbagh et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited
This paper presents a novel features mining approach from documents that could not be mined via optical character recognition(OCR) By identifying the intimate relationship between the text and graphical components the proposed technique pulls out theStart End and Exact values for each bar Furthermore the word 2-gram and Euclidean distance methods are used to accuratelydetect and determine plagiarism in bar charts
1 Introduction
Detection determination and rectification of plagiarism areoutstanding quests in every sphere of documentation andcopyright Lately the significant advancement in informationtechnology represented by digital libraries and World WideWeb is regarded as one of the main reasons for exponentialgrowth in plagiarism appearance It has become effortless forthe plagiarist to utilize or copy the work of others withoutacknowledging or citing them due to the easy availabilityof most resources in digital format Thus plagiarism isregarded as one of the electronic crimes and intellectualthefts from others documents [1ndash3] In academia plagiarismposed a severe educational challengewhich is acutely faced byresearch institutions universities and even schools Severalefforts are dedicated to detecting different types of plagiarismvia programming code and text Plagiarism detection beganin the 1970s where the identification of rate of plagiarismin programming code written by some computer languagessuch as C and Pascal was introduced [4] Digital documents
being the major carriers of information require extremeauthentication in terms of their origins and trustfulness Thequest for achieving an accurate and efficient image forgerydetection method in digital documentation is never endingDeveloping a robust plagiarism detector by overcoming thelimitations associated with human intervention is the keyissue [5]
Recently several researchers developed the algorithmicapproach using computer codes to detect plagiarism in thehomework of students [6] Based on levels of plagiarism pat-terns some studies introduced plagiarism detection methodswhich are implemented in the algorithms and program codes[7] Generally the computerized or statistical approaches areexploited to detect plagiarism in natural language since the1990s The techniques used for natural language are basedon various factors such as grammar semantic and grammar-semantics hybridizations [1 4] However the grammar-basedmethod is one of the restrictive ones to detect plagiarismThistype of method analyzes the sentences based on grammaticalstructure which can be efficiently used to detect the Exact
Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 612787 11 pageshttpdxdoiorg1011552014612787
2 The Scientific World Journal
Summarising caption
Synonyms of caption
Restructuring caption sentence
With caption
Without captionAll
chart
Part of chart
Exact copy
Modified copy
Chart plagiarism
Translation from one language to others
All data in chart
Part of data in chart
Restructuring chart
Same shape
Different shape
Changing color
Changing scale
Change words and symbols
Changing words via synonyms
Changing among symbols
Change value within range
Changing part of colors for chart
Changing all colors of chart
Convert to text
Convert among chart types
Convert to paragraph
Convert to table
All data converted
Part of data converted
Same colors
Different colors
All data
Part of data
Arrange manner
Arbitrary manner
All data
Part of data
Figure 1 Taxonomy of chart plagiarism
Copy of text While semantic-based method utilizes vectorspace model to calculate the similarities among the textsUndoubtedly the grammar-semantics hybrid approach over-comes all disadvantages of the other methods This is con-sidered as one of the most versatile techniques to detect textplagiarism [1 8 9]
A new taxonomy is introduced to explain the concepts forvarious types and patterns of text plagiarism [4] Plagiarismis divided into twomain parts including literal and intelligentone Each part consists of several subparts which cover allpossibilities of text plagiarism Generally the representationsof quantitative information are formulated via infographicformby using figures charts and tablesThe information thatis displayed in charts figures and tables includes results ofexperiments framework and statistical facts These data andinformation in homogenous form can be formulated by usingvarious shapes such as pie chart bar chart and 2D and 3Dplots [10ndash12]
We report a new type of plagiarism detection methodby highlighting the types of information that can be stolenfrom others work without referencing Firstly different typesof forged information are organized into taxonomy of chart
figure and table to highlight varieties of plagiarism patternssuch as Exact and Modified Copy Secondly plagiarismdetection in bar chart image is performed depending on tenfeatures in images Some of the features are extracted byOCR tool while others are acquired from the relationship oftext and graphic components [13 14] Finally the proposedtechnique is used to extract the features of bar chart imageswhich cannot be extracted by OCR to detect plagiarism Thepaper is organized as follows Section 2 describes variousexisting techniques for extracting data from bar chart images[15] The taxonomy of chart figure and table related toplagiarism is presented in Section 3 Section 4 discussesthe methodology and Section 5 includes the experimentalresults of bar chart plagiarism detection The discussions areelucidated in Section 6 Section 7 concludes the paper
2 Related Work
Categorizations of bar chart images refer to their labelinginto one of the predefined geometrical or nongeometricalclasses Though the classification is apparently manageable
The Scientific World Journal 3
Figure plagiarism
Modified copy
Exact copy
All figure
Part of figure
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Translation from one language to others
All data in figure
Part of data in figure
Restructuring figure
Same shape
Different shape
Convert to other shapes
Same colors
Different colors
Convert to paragraph
All data
Part of data
Changing color
Change words and sentences
Swap amongcomponents
of figure
All data
Part of data
Change by synonyms
Change by summarising
Change by restructuring
Figure 2 Taxonomy of figure plagiarism
it is proven to be an extremely difficult problem in com-puter programming Hence there is an intense attention indeveloping automatic tools to categorize describe or retrieveimages based on their contents
Consequently researchers attempted to extract the fea-tures and data from chart images For automatic images cat-egorization and description computational model is success-fully introduced [16] The analysis of local and global imagecharacteristics by using text and image features is used in themodel The model is capable of differentiating geometricaland ordinary imagesThe computational model is comprisedof classifier stage which is trained by the associated textfeatures using advanced concepts and similarity matchingstage
Classification methods based on multiple-instance learn-ing for chart images are also developed [10] A re-revision
system consisting of three concatenated major stages suchas classification extraction and redesigned chart images isemployed [17] In the extraction stage two types of charts (pieand bar) are focused on Some techniques are presented inextracting data and graphicalmarks from chart images Trulythe understanding and recognition of chart images requirethe preprocessing and extraction of data and informationPrimarily two types of available methods that deal with chartimages are either to consider electronic chart directly [18ndash20]or to obtain them after converting into raster images [21ndash24]Mishchenko and Vassilieva [25] introduced a model-basedmethod for the classification of chart images which involvedtwomain stages firstly predicting the location and the size ofchart depending on the color distribution of chart image andsecondly the extraction andmatching of chart image edges toachieve the best match between query and database images
4 The Scientific World Journal
Table plagiarism
Modified copy
Exact copy
All table
Part of table
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Copy column(s) from table
Copy row(s) from table
Copy columns and rows from table
Translation from one language to others
Restructuring table
Same shape
Different shape
Convert to paragraph
All table
Part of table
Convert to chart
All data
Part of data
All data
Part of data
Swapping among cells
Change words and sentences
Used synonyms
Summarising
Restructuring
Swapping of columns
Swapping of rows
Swap row(s) with column(s)
Figure 3 Taxonomy of table plagiarism
The techniques for features extraction of image dependonthe type of images such as chart or medical representationSome techniques are applicable on two-dimensional plot ofchart images while others work well for bar chart imagesHough transform technique is introduced as an approachto extract the features of bar chart images [23] Someinvestigations are based on the edges of bars to extract thefeatures [24 26] The learning-based method is establishedto recognize the chart images [22] The features of bar chartimages can be extracted by describing the height andwidth ofeach bar which is applied on statistical images to determinethe similarities [27] Meanwhile other techniques focusedon geometric features rather than data and information ofscientific bar chart images [28]
Currently several techniques are developed to extract thefeatures frommedical imagesThe texture is one of the visual
contents of a medical image used in content-based imageretrieval (CBIR) to represent the image effectively for search-ing and recovering similar areas [29] Gray-level statisticalmatrix technique is applied to extract the texture informationfor the content-based retrieval of mammograms from theMIAS database [30] 3D texture features technique based onthe cooccurrence matrixes of the gray-level gradient andcurvature information regarding the nodule volume data forclassifying themalignancy from benign is introduced [31 32]
3 Plagiarism Taxonomy and Patterns
Three types of graphic plagiarism such as figure chartand table are important to emphasize Each type highlightsdifferent levels of plagiarism The patterns and types ofplagiarism for figures charts and tables are presented as
The Scientific World Journal 5
taxonomy Some kinds of text plagiarism are also evaluated[33] The methods for detecting passages of text plagiarismfor documents without appropriate citations are also sug-gested Taxonomy is further extended to cover other typesof plagiarism [4] The taxonomy presented in various studiesmajorly demonstrates literal and intelligent plagiarism whereeach kind includes many patterns of plagiarism Howeverwe are interested in detecting plagiarism of charts as well astheir taxonomy figures and tables Alternatively charts (piebar and line) can be considered as one of the methods forrepresenting the data and information of experimental resultsor comparing among techniques which are copied from otherreferences without citation Therefore plagiarism of chartscan be formulated in several forms to manifest the sameinformation in various shapes Taxonomy of chart plagiarismdemonstrates many patterns and models which may be usedto plagiarize the data of chart image
Plagiarism patterns of chart figure and table are dividedinto Exact Copy and Modified Copy prototypes The ExactCopy patterns of plagiarism are defined as the direct quoteof data from other works without referencing where copyand paste of the whole or part of the information image isperformed Simplicity is one of the important attributes ofthis type of plagiarism Besides this type of plagiarism doesnot require much time to hide the academic crimeThe othertype of graphic plagiarism is the Modified Copy for infor-mation of chart figure and table This is more intelligentlyperformed than the previous one because the same data canbe formulated in many ways to exhibit the work in a differentstyle than the original oneThe goal of these intelligentmeansis that the plagiarist attempts to deceive the readers by doingsome changes such as translation from other languages orgenerating another shape for the same data
TheModifiedCopy plagiarisms are primarily divided intotranslation and restructuring In this research new types ofcopying are organized by taxonomy which explains variouspatterns of graphic plagiarism Furthermore the primaryfocus of the bar chart image is to detect the proportion ofplagarism Figures 1 2 and 3 depict the taxonomy of chartfigure and table plagiarism respectively
4 Methodology
Themethodology of bar chart plagiarism detection as shownin Figure 4 consists of three main stages namely planningand collection feature extraction and development withsystem evaluation In the planning and collection stagevarious patterns of graphical plagiarism via taxonomy ofchart figures and table are presented (Figures 1 2 and 3)Thetaxonomy of chart plagiarism explains different formulationswhich plagiarize the data of bar chart images Thereforevarieties of bar chart images are collected and data sets areconsidered The gatherings of the data sets consist of 100 barchart images for storing in databases and twenty images forquery including all possibilities of plagiarism for bar chartimages These data sets are collected from different resourcessuch as thesis which represented various types of bar chartimages in 2D and 3D Besides vertical and horizontal barchart images as shown in Figure 5 are taken into account
Planning and collection
Extract text features
Preprocessing
Extract numeric features
Detect plagiarism
System evaluation
Feat
ures
extr
actio
nD
evelo
pmen
t an
d ev
alua
tion
Figure 4 Flowchart of methodology
In the feature extraction stage the features of bar chartimages are used to detect plagiarism Various types of barchart images are analyzed to detect the features of imageThebar chart images inferred to acquiremaximum of ten featuresrepresenting the information and the data of image Thesefeatures are common in different types of bar chart imagesfor instance in 2D and 3D images However the number ofuses for these features may different from each other
The features extraction is an essential process to get thedata from images which can be utilized to detect the rate ofplagiarized data Therefore these ten features are categorizedinto low-level and high-level features The low-level featuresrefer to the text features of bar chart The text features are thetext which can be used in image to represent the informationand data such as caption of image label of each bar label ofcoordinates and values on coordinates Generally they areextracted from bar chart images using OCR tool Converselythe high-level features referring to numeric features cannot beextracted using OCR toolThe extraction of numeric featuresrequires a relationship between the text and graphic compo-nents The numeric features include values of bars in imageEach bar in an image has three numeric features which canbe extracted by the proposed technique depending on StartEnd and Exact values The Start and End values representthe first and last values while the Exact one corresponds tothe real value of the bar For instance the text features forimage in Figure 5 are ldquoFigure 10 Revision Design GalleriesrdquoldquoSilver Platinum Cashrdquo and ldquo0 5 40rdquo whichrepresent caption of image and label of coordinates X and Yrespectively Meanwhile the numeric features represent thevalue of each bar which can be detected by Start End andExact features For example the Start End and Exact featuresof bar Silver are 5 10 and 6 respectively
These features are used to detect the proportion of pla-giarism for bar chart imageThe extraction of Start End and
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
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[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
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2 The Scientific World Journal
Summarising caption
Synonyms of caption
Restructuring caption sentence
With caption
Without captionAll
chart
Part of chart
Exact copy
Modified copy
Chart plagiarism
Translation from one language to others
All data in chart
Part of data in chart
Restructuring chart
Same shape
Different shape
Changing color
Changing scale
Change words and symbols
Changing words via synonyms
Changing among symbols
Change value within range
Changing part of colors for chart
Changing all colors of chart
Convert to text
Convert among chart types
Convert to paragraph
Convert to table
All data converted
Part of data converted
Same colors
Different colors
All data
Part of data
Arrange manner
Arbitrary manner
All data
Part of data
Figure 1 Taxonomy of chart plagiarism
Copy of text While semantic-based method utilizes vectorspace model to calculate the similarities among the textsUndoubtedly the grammar-semantics hybrid approach over-comes all disadvantages of the other methods This is con-sidered as one of the most versatile techniques to detect textplagiarism [1 8 9]
A new taxonomy is introduced to explain the concepts forvarious types and patterns of text plagiarism [4] Plagiarismis divided into twomain parts including literal and intelligentone Each part consists of several subparts which cover allpossibilities of text plagiarism Generally the representationsof quantitative information are formulated via infographicformby using figures charts and tablesThe information thatis displayed in charts figures and tables includes results ofexperiments framework and statistical facts These data andinformation in homogenous form can be formulated by usingvarious shapes such as pie chart bar chart and 2D and 3Dplots [10ndash12]
We report a new type of plagiarism detection methodby highlighting the types of information that can be stolenfrom others work without referencing Firstly different typesof forged information are organized into taxonomy of chart
figure and table to highlight varieties of plagiarism patternssuch as Exact and Modified Copy Secondly plagiarismdetection in bar chart image is performed depending on tenfeatures in images Some of the features are extracted byOCR tool while others are acquired from the relationship oftext and graphic components [13 14] Finally the proposedtechnique is used to extract the features of bar chart imageswhich cannot be extracted by OCR to detect plagiarism Thepaper is organized as follows Section 2 describes variousexisting techniques for extracting data from bar chart images[15] The taxonomy of chart figure and table related toplagiarism is presented in Section 3 Section 4 discussesthe methodology and Section 5 includes the experimentalresults of bar chart plagiarism detection The discussions areelucidated in Section 6 Section 7 concludes the paper
2 Related Work
Categorizations of bar chart images refer to their labelinginto one of the predefined geometrical or nongeometricalclasses Though the classification is apparently manageable
The Scientific World Journal 3
Figure plagiarism
Modified copy
Exact copy
All figure
Part of figure
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Translation from one language to others
All data in figure
Part of data in figure
Restructuring figure
Same shape
Different shape
Convert to other shapes
Same colors
Different colors
Convert to paragraph
All data
Part of data
Changing color
Change words and sentences
Swap amongcomponents
of figure
All data
Part of data
Change by synonyms
Change by summarising
Change by restructuring
Figure 2 Taxonomy of figure plagiarism
it is proven to be an extremely difficult problem in com-puter programming Hence there is an intense attention indeveloping automatic tools to categorize describe or retrieveimages based on their contents
Consequently researchers attempted to extract the fea-tures and data from chart images For automatic images cat-egorization and description computational model is success-fully introduced [16] The analysis of local and global imagecharacteristics by using text and image features is used in themodel The model is capable of differentiating geometricaland ordinary imagesThe computational model is comprisedof classifier stage which is trained by the associated textfeatures using advanced concepts and similarity matchingstage
Classification methods based on multiple-instance learn-ing for chart images are also developed [10] A re-revision
system consisting of three concatenated major stages suchas classification extraction and redesigned chart images isemployed [17] In the extraction stage two types of charts (pieand bar) are focused on Some techniques are presented inextracting data and graphicalmarks from chart images Trulythe understanding and recognition of chart images requirethe preprocessing and extraction of data and informationPrimarily two types of available methods that deal with chartimages are either to consider electronic chart directly [18ndash20]or to obtain them after converting into raster images [21ndash24]Mishchenko and Vassilieva [25] introduced a model-basedmethod for the classification of chart images which involvedtwomain stages firstly predicting the location and the size ofchart depending on the color distribution of chart image andsecondly the extraction andmatching of chart image edges toachieve the best match between query and database images
4 The Scientific World Journal
Table plagiarism
Modified copy
Exact copy
All table
Part of table
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Copy column(s) from table
Copy row(s) from table
Copy columns and rows from table
Translation from one language to others
Restructuring table
Same shape
Different shape
Convert to paragraph
All table
Part of table
Convert to chart
All data
Part of data
All data
Part of data
Swapping among cells
Change words and sentences
Used synonyms
Summarising
Restructuring
Swapping of columns
Swapping of rows
Swap row(s) with column(s)
Figure 3 Taxonomy of table plagiarism
The techniques for features extraction of image dependonthe type of images such as chart or medical representationSome techniques are applicable on two-dimensional plot ofchart images while others work well for bar chart imagesHough transform technique is introduced as an approachto extract the features of bar chart images [23] Someinvestigations are based on the edges of bars to extract thefeatures [24 26] The learning-based method is establishedto recognize the chart images [22] The features of bar chartimages can be extracted by describing the height andwidth ofeach bar which is applied on statistical images to determinethe similarities [27] Meanwhile other techniques focusedon geometric features rather than data and information ofscientific bar chart images [28]
Currently several techniques are developed to extract thefeatures frommedical imagesThe texture is one of the visual
contents of a medical image used in content-based imageretrieval (CBIR) to represent the image effectively for search-ing and recovering similar areas [29] Gray-level statisticalmatrix technique is applied to extract the texture informationfor the content-based retrieval of mammograms from theMIAS database [30] 3D texture features technique based onthe cooccurrence matrixes of the gray-level gradient andcurvature information regarding the nodule volume data forclassifying themalignancy from benign is introduced [31 32]
3 Plagiarism Taxonomy and Patterns
Three types of graphic plagiarism such as figure chartand table are important to emphasize Each type highlightsdifferent levels of plagiarism The patterns and types ofplagiarism for figures charts and tables are presented as
The Scientific World Journal 5
taxonomy Some kinds of text plagiarism are also evaluated[33] The methods for detecting passages of text plagiarismfor documents without appropriate citations are also sug-gested Taxonomy is further extended to cover other typesof plagiarism [4] The taxonomy presented in various studiesmajorly demonstrates literal and intelligent plagiarism whereeach kind includes many patterns of plagiarism Howeverwe are interested in detecting plagiarism of charts as well astheir taxonomy figures and tables Alternatively charts (piebar and line) can be considered as one of the methods forrepresenting the data and information of experimental resultsor comparing among techniques which are copied from otherreferences without citation Therefore plagiarism of chartscan be formulated in several forms to manifest the sameinformation in various shapes Taxonomy of chart plagiarismdemonstrates many patterns and models which may be usedto plagiarize the data of chart image
Plagiarism patterns of chart figure and table are dividedinto Exact Copy and Modified Copy prototypes The ExactCopy patterns of plagiarism are defined as the direct quoteof data from other works without referencing where copyand paste of the whole or part of the information image isperformed Simplicity is one of the important attributes ofthis type of plagiarism Besides this type of plagiarism doesnot require much time to hide the academic crimeThe othertype of graphic plagiarism is the Modified Copy for infor-mation of chart figure and table This is more intelligentlyperformed than the previous one because the same data canbe formulated in many ways to exhibit the work in a differentstyle than the original oneThe goal of these intelligentmeansis that the plagiarist attempts to deceive the readers by doingsome changes such as translation from other languages orgenerating another shape for the same data
TheModifiedCopy plagiarisms are primarily divided intotranslation and restructuring In this research new types ofcopying are organized by taxonomy which explains variouspatterns of graphic plagiarism Furthermore the primaryfocus of the bar chart image is to detect the proportion ofplagarism Figures 1 2 and 3 depict the taxonomy of chartfigure and table plagiarism respectively
4 Methodology
Themethodology of bar chart plagiarism detection as shownin Figure 4 consists of three main stages namely planningand collection feature extraction and development withsystem evaluation In the planning and collection stagevarious patterns of graphical plagiarism via taxonomy ofchart figures and table are presented (Figures 1 2 and 3)Thetaxonomy of chart plagiarism explains different formulationswhich plagiarize the data of bar chart images Thereforevarieties of bar chart images are collected and data sets areconsidered The gatherings of the data sets consist of 100 barchart images for storing in databases and twenty images forquery including all possibilities of plagiarism for bar chartimages These data sets are collected from different resourcessuch as thesis which represented various types of bar chartimages in 2D and 3D Besides vertical and horizontal barchart images as shown in Figure 5 are taken into account
Planning and collection
Extract text features
Preprocessing
Extract numeric features
Detect plagiarism
System evaluation
Feat
ures
extr
actio
nD
evelo
pmen
t an
d ev
alua
tion
Figure 4 Flowchart of methodology
In the feature extraction stage the features of bar chartimages are used to detect plagiarism Various types of barchart images are analyzed to detect the features of imageThebar chart images inferred to acquiremaximum of ten featuresrepresenting the information and the data of image Thesefeatures are common in different types of bar chart imagesfor instance in 2D and 3D images However the number ofuses for these features may different from each other
The features extraction is an essential process to get thedata from images which can be utilized to detect the rate ofplagiarized data Therefore these ten features are categorizedinto low-level and high-level features The low-level featuresrefer to the text features of bar chart The text features are thetext which can be used in image to represent the informationand data such as caption of image label of each bar label ofcoordinates and values on coordinates Generally they areextracted from bar chart images using OCR tool Converselythe high-level features referring to numeric features cannot beextracted using OCR toolThe extraction of numeric featuresrequires a relationship between the text and graphic compo-nents The numeric features include values of bars in imageEach bar in an image has three numeric features which canbe extracted by the proposed technique depending on StartEnd and Exact values The Start and End values representthe first and last values while the Exact one corresponds tothe real value of the bar For instance the text features forimage in Figure 5 are ldquoFigure 10 Revision Design GalleriesrdquoldquoSilver Platinum Cashrdquo and ldquo0 5 40rdquo whichrepresent caption of image and label of coordinates X and Yrespectively Meanwhile the numeric features represent thevalue of each bar which can be detected by Start End andExact features For example the Start End and Exact featuresof bar Silver are 5 10 and 6 respectively
These features are used to detect the proportion of pla-giarism for bar chart imageThe extraction of Start End and
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
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RoboticsJournal of
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
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The Scientific World Journal 3
Figure plagiarism
Modified copy
Exact copy
All figure
Part of figure
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Translation from one language to others
All data in figure
Part of data in figure
Restructuring figure
Same shape
Different shape
Convert to other shapes
Same colors
Different colors
Convert to paragraph
All data
Part of data
Changing color
Change words and sentences
Swap amongcomponents
of figure
All data
Part of data
Change by synonyms
Change by summarising
Change by restructuring
Figure 2 Taxonomy of figure plagiarism
it is proven to be an extremely difficult problem in com-puter programming Hence there is an intense attention indeveloping automatic tools to categorize describe or retrieveimages based on their contents
Consequently researchers attempted to extract the fea-tures and data from chart images For automatic images cat-egorization and description computational model is success-fully introduced [16] The analysis of local and global imagecharacteristics by using text and image features is used in themodel The model is capable of differentiating geometricaland ordinary imagesThe computational model is comprisedof classifier stage which is trained by the associated textfeatures using advanced concepts and similarity matchingstage
Classification methods based on multiple-instance learn-ing for chart images are also developed [10] A re-revision
system consisting of three concatenated major stages suchas classification extraction and redesigned chart images isemployed [17] In the extraction stage two types of charts (pieand bar) are focused on Some techniques are presented inextracting data and graphicalmarks from chart images Trulythe understanding and recognition of chart images requirethe preprocessing and extraction of data and informationPrimarily two types of available methods that deal with chartimages are either to consider electronic chart directly [18ndash20]or to obtain them after converting into raster images [21ndash24]Mishchenko and Vassilieva [25] introduced a model-basedmethod for the classification of chart images which involvedtwomain stages firstly predicting the location and the size ofchart depending on the color distribution of chart image andsecondly the extraction andmatching of chart image edges toachieve the best match between query and database images
4 The Scientific World Journal
Table plagiarism
Modified copy
Exact copy
All table
Part of table
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Copy column(s) from table
Copy row(s) from table
Copy columns and rows from table
Translation from one language to others
Restructuring table
Same shape
Different shape
Convert to paragraph
All table
Part of table
Convert to chart
All data
Part of data
All data
Part of data
Swapping among cells
Change words and sentences
Used synonyms
Summarising
Restructuring
Swapping of columns
Swapping of rows
Swap row(s) with column(s)
Figure 3 Taxonomy of table plagiarism
The techniques for features extraction of image dependonthe type of images such as chart or medical representationSome techniques are applicable on two-dimensional plot ofchart images while others work well for bar chart imagesHough transform technique is introduced as an approachto extract the features of bar chart images [23] Someinvestigations are based on the edges of bars to extract thefeatures [24 26] The learning-based method is establishedto recognize the chart images [22] The features of bar chartimages can be extracted by describing the height andwidth ofeach bar which is applied on statistical images to determinethe similarities [27] Meanwhile other techniques focusedon geometric features rather than data and information ofscientific bar chart images [28]
Currently several techniques are developed to extract thefeatures frommedical imagesThe texture is one of the visual
contents of a medical image used in content-based imageretrieval (CBIR) to represent the image effectively for search-ing and recovering similar areas [29] Gray-level statisticalmatrix technique is applied to extract the texture informationfor the content-based retrieval of mammograms from theMIAS database [30] 3D texture features technique based onthe cooccurrence matrixes of the gray-level gradient andcurvature information regarding the nodule volume data forclassifying themalignancy from benign is introduced [31 32]
3 Plagiarism Taxonomy and Patterns
Three types of graphic plagiarism such as figure chartand table are important to emphasize Each type highlightsdifferent levels of plagiarism The patterns and types ofplagiarism for figures charts and tables are presented as
The Scientific World Journal 5
taxonomy Some kinds of text plagiarism are also evaluated[33] The methods for detecting passages of text plagiarismfor documents without appropriate citations are also sug-gested Taxonomy is further extended to cover other typesof plagiarism [4] The taxonomy presented in various studiesmajorly demonstrates literal and intelligent plagiarism whereeach kind includes many patterns of plagiarism Howeverwe are interested in detecting plagiarism of charts as well astheir taxonomy figures and tables Alternatively charts (piebar and line) can be considered as one of the methods forrepresenting the data and information of experimental resultsor comparing among techniques which are copied from otherreferences without citation Therefore plagiarism of chartscan be formulated in several forms to manifest the sameinformation in various shapes Taxonomy of chart plagiarismdemonstrates many patterns and models which may be usedto plagiarize the data of chart image
Plagiarism patterns of chart figure and table are dividedinto Exact Copy and Modified Copy prototypes The ExactCopy patterns of plagiarism are defined as the direct quoteof data from other works without referencing where copyand paste of the whole or part of the information image isperformed Simplicity is one of the important attributes ofthis type of plagiarism Besides this type of plagiarism doesnot require much time to hide the academic crimeThe othertype of graphic plagiarism is the Modified Copy for infor-mation of chart figure and table This is more intelligentlyperformed than the previous one because the same data canbe formulated in many ways to exhibit the work in a differentstyle than the original oneThe goal of these intelligentmeansis that the plagiarist attempts to deceive the readers by doingsome changes such as translation from other languages orgenerating another shape for the same data
TheModifiedCopy plagiarisms are primarily divided intotranslation and restructuring In this research new types ofcopying are organized by taxonomy which explains variouspatterns of graphic plagiarism Furthermore the primaryfocus of the bar chart image is to detect the proportion ofplagarism Figures 1 2 and 3 depict the taxonomy of chartfigure and table plagiarism respectively
4 Methodology
Themethodology of bar chart plagiarism detection as shownin Figure 4 consists of three main stages namely planningand collection feature extraction and development withsystem evaluation In the planning and collection stagevarious patterns of graphical plagiarism via taxonomy ofchart figures and table are presented (Figures 1 2 and 3)Thetaxonomy of chart plagiarism explains different formulationswhich plagiarize the data of bar chart images Thereforevarieties of bar chart images are collected and data sets areconsidered The gatherings of the data sets consist of 100 barchart images for storing in databases and twenty images forquery including all possibilities of plagiarism for bar chartimages These data sets are collected from different resourcessuch as thesis which represented various types of bar chartimages in 2D and 3D Besides vertical and horizontal barchart images as shown in Figure 5 are taken into account
Planning and collection
Extract text features
Preprocessing
Extract numeric features
Detect plagiarism
System evaluation
Feat
ures
extr
actio
nD
evelo
pmen
t an
d ev
alua
tion
Figure 4 Flowchart of methodology
In the feature extraction stage the features of bar chartimages are used to detect plagiarism Various types of barchart images are analyzed to detect the features of imageThebar chart images inferred to acquiremaximum of ten featuresrepresenting the information and the data of image Thesefeatures are common in different types of bar chart imagesfor instance in 2D and 3D images However the number ofuses for these features may different from each other
The features extraction is an essential process to get thedata from images which can be utilized to detect the rate ofplagiarized data Therefore these ten features are categorizedinto low-level and high-level features The low-level featuresrefer to the text features of bar chart The text features are thetext which can be used in image to represent the informationand data such as caption of image label of each bar label ofcoordinates and values on coordinates Generally they areextracted from bar chart images using OCR tool Converselythe high-level features referring to numeric features cannot beextracted using OCR toolThe extraction of numeric featuresrequires a relationship between the text and graphic compo-nents The numeric features include values of bars in imageEach bar in an image has three numeric features which canbe extracted by the proposed technique depending on StartEnd and Exact values The Start and End values representthe first and last values while the Exact one corresponds tothe real value of the bar For instance the text features forimage in Figure 5 are ldquoFigure 10 Revision Design GalleriesrdquoldquoSilver Platinum Cashrdquo and ldquo0 5 40rdquo whichrepresent caption of image and label of coordinates X and Yrespectively Meanwhile the numeric features represent thevalue of each bar which can be detected by Start End andExact features For example the Start End and Exact featuresof bar Silver are 5 10 and 6 respectively
These features are used to detect the proportion of pla-giarism for bar chart imageThe extraction of Start End and
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
4 The Scientific World Journal
Table plagiarism
Modified copy
Exact copy
All table
Part of table
Without caption
With caption
Summarising caption
Synonyms of caption
Restructuring caption
Copy column(s) from table
Copy row(s) from table
Copy columns and rows from table
Translation from one language to others
Restructuring table
Same shape
Different shape
Convert to paragraph
All table
Part of table
Convert to chart
All data
Part of data
All data
Part of data
Swapping among cells
Change words and sentences
Used synonyms
Summarising
Restructuring
Swapping of columns
Swapping of rows
Swap row(s) with column(s)
Figure 3 Taxonomy of table plagiarism
The techniques for features extraction of image dependonthe type of images such as chart or medical representationSome techniques are applicable on two-dimensional plot ofchart images while others work well for bar chart imagesHough transform technique is introduced as an approachto extract the features of bar chart images [23] Someinvestigations are based on the edges of bars to extract thefeatures [24 26] The learning-based method is establishedto recognize the chart images [22] The features of bar chartimages can be extracted by describing the height andwidth ofeach bar which is applied on statistical images to determinethe similarities [27] Meanwhile other techniques focusedon geometric features rather than data and information ofscientific bar chart images [28]
Currently several techniques are developed to extract thefeatures frommedical imagesThe texture is one of the visual
contents of a medical image used in content-based imageretrieval (CBIR) to represent the image effectively for search-ing and recovering similar areas [29] Gray-level statisticalmatrix technique is applied to extract the texture informationfor the content-based retrieval of mammograms from theMIAS database [30] 3D texture features technique based onthe cooccurrence matrixes of the gray-level gradient andcurvature information regarding the nodule volume data forclassifying themalignancy from benign is introduced [31 32]
3 Plagiarism Taxonomy and Patterns
Three types of graphic plagiarism such as figure chartand table are important to emphasize Each type highlightsdifferent levels of plagiarism The patterns and types ofplagiarism for figures charts and tables are presented as
The Scientific World Journal 5
taxonomy Some kinds of text plagiarism are also evaluated[33] The methods for detecting passages of text plagiarismfor documents without appropriate citations are also sug-gested Taxonomy is further extended to cover other typesof plagiarism [4] The taxonomy presented in various studiesmajorly demonstrates literal and intelligent plagiarism whereeach kind includes many patterns of plagiarism Howeverwe are interested in detecting plagiarism of charts as well astheir taxonomy figures and tables Alternatively charts (piebar and line) can be considered as one of the methods forrepresenting the data and information of experimental resultsor comparing among techniques which are copied from otherreferences without citation Therefore plagiarism of chartscan be formulated in several forms to manifest the sameinformation in various shapes Taxonomy of chart plagiarismdemonstrates many patterns and models which may be usedto plagiarize the data of chart image
Plagiarism patterns of chart figure and table are dividedinto Exact Copy and Modified Copy prototypes The ExactCopy patterns of plagiarism are defined as the direct quoteof data from other works without referencing where copyand paste of the whole or part of the information image isperformed Simplicity is one of the important attributes ofthis type of plagiarism Besides this type of plagiarism doesnot require much time to hide the academic crimeThe othertype of graphic plagiarism is the Modified Copy for infor-mation of chart figure and table This is more intelligentlyperformed than the previous one because the same data canbe formulated in many ways to exhibit the work in a differentstyle than the original oneThe goal of these intelligentmeansis that the plagiarist attempts to deceive the readers by doingsome changes such as translation from other languages orgenerating another shape for the same data
TheModifiedCopy plagiarisms are primarily divided intotranslation and restructuring In this research new types ofcopying are organized by taxonomy which explains variouspatterns of graphic plagiarism Furthermore the primaryfocus of the bar chart image is to detect the proportion ofplagarism Figures 1 2 and 3 depict the taxonomy of chartfigure and table plagiarism respectively
4 Methodology
Themethodology of bar chart plagiarism detection as shownin Figure 4 consists of three main stages namely planningand collection feature extraction and development withsystem evaluation In the planning and collection stagevarious patterns of graphical plagiarism via taxonomy ofchart figures and table are presented (Figures 1 2 and 3)Thetaxonomy of chart plagiarism explains different formulationswhich plagiarize the data of bar chart images Thereforevarieties of bar chart images are collected and data sets areconsidered The gatherings of the data sets consist of 100 barchart images for storing in databases and twenty images forquery including all possibilities of plagiarism for bar chartimages These data sets are collected from different resourcessuch as thesis which represented various types of bar chartimages in 2D and 3D Besides vertical and horizontal barchart images as shown in Figure 5 are taken into account
Planning and collection
Extract text features
Preprocessing
Extract numeric features
Detect plagiarism
System evaluation
Feat
ures
extr
actio
nD
evelo
pmen
t an
d ev
alua
tion
Figure 4 Flowchart of methodology
In the feature extraction stage the features of bar chartimages are used to detect plagiarism Various types of barchart images are analyzed to detect the features of imageThebar chart images inferred to acquiremaximum of ten featuresrepresenting the information and the data of image Thesefeatures are common in different types of bar chart imagesfor instance in 2D and 3D images However the number ofuses for these features may different from each other
The features extraction is an essential process to get thedata from images which can be utilized to detect the rate ofplagiarized data Therefore these ten features are categorizedinto low-level and high-level features The low-level featuresrefer to the text features of bar chart The text features are thetext which can be used in image to represent the informationand data such as caption of image label of each bar label ofcoordinates and values on coordinates Generally they areextracted from bar chart images using OCR tool Converselythe high-level features referring to numeric features cannot beextracted using OCR toolThe extraction of numeric featuresrequires a relationship between the text and graphic compo-nents The numeric features include values of bars in imageEach bar in an image has three numeric features which canbe extracted by the proposed technique depending on StartEnd and Exact values The Start and End values representthe first and last values while the Exact one corresponds tothe real value of the bar For instance the text features forimage in Figure 5 are ldquoFigure 10 Revision Design GalleriesrdquoldquoSilver Platinum Cashrdquo and ldquo0 5 40rdquo whichrepresent caption of image and label of coordinates X and Yrespectively Meanwhile the numeric features represent thevalue of each bar which can be detected by Start End andExact features For example the Start End and Exact featuresof bar Silver are 5 10 and 6 respectively
These features are used to detect the proportion of pla-giarism for bar chart imageThe extraction of Start End and
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
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Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
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Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
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International Journal of
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ArtificialNeural Systems
Advances in
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RoboticsJournal of
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Industrial EngineeringJournal of
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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Human-ComputerInteraction
Advances in
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The Scientific World Journal 5
taxonomy Some kinds of text plagiarism are also evaluated[33] The methods for detecting passages of text plagiarismfor documents without appropriate citations are also sug-gested Taxonomy is further extended to cover other typesof plagiarism [4] The taxonomy presented in various studiesmajorly demonstrates literal and intelligent plagiarism whereeach kind includes many patterns of plagiarism Howeverwe are interested in detecting plagiarism of charts as well astheir taxonomy figures and tables Alternatively charts (piebar and line) can be considered as one of the methods forrepresenting the data and information of experimental resultsor comparing among techniques which are copied from otherreferences without citation Therefore plagiarism of chartscan be formulated in several forms to manifest the sameinformation in various shapes Taxonomy of chart plagiarismdemonstrates many patterns and models which may be usedto plagiarize the data of chart image
Plagiarism patterns of chart figure and table are dividedinto Exact Copy and Modified Copy prototypes The ExactCopy patterns of plagiarism are defined as the direct quoteof data from other works without referencing where copyand paste of the whole or part of the information image isperformed Simplicity is one of the important attributes ofthis type of plagiarism Besides this type of plagiarism doesnot require much time to hide the academic crimeThe othertype of graphic plagiarism is the Modified Copy for infor-mation of chart figure and table This is more intelligentlyperformed than the previous one because the same data canbe formulated in many ways to exhibit the work in a differentstyle than the original oneThe goal of these intelligentmeansis that the plagiarist attempts to deceive the readers by doingsome changes such as translation from other languages orgenerating another shape for the same data
TheModifiedCopy plagiarisms are primarily divided intotranslation and restructuring In this research new types ofcopying are organized by taxonomy which explains variouspatterns of graphic plagiarism Furthermore the primaryfocus of the bar chart image is to detect the proportion ofplagarism Figures 1 2 and 3 depict the taxonomy of chartfigure and table plagiarism respectively
4 Methodology
Themethodology of bar chart plagiarism detection as shownin Figure 4 consists of three main stages namely planningand collection feature extraction and development withsystem evaluation In the planning and collection stagevarious patterns of graphical plagiarism via taxonomy ofchart figures and table are presented (Figures 1 2 and 3)Thetaxonomy of chart plagiarism explains different formulationswhich plagiarize the data of bar chart images Thereforevarieties of bar chart images are collected and data sets areconsidered The gatherings of the data sets consist of 100 barchart images for storing in databases and twenty images forquery including all possibilities of plagiarism for bar chartimages These data sets are collected from different resourcessuch as thesis which represented various types of bar chartimages in 2D and 3D Besides vertical and horizontal barchart images as shown in Figure 5 are taken into account
Planning and collection
Extract text features
Preprocessing
Extract numeric features
Detect plagiarism
System evaluation
Feat
ures
extr
actio
nD
evelo
pmen
t an
d ev
alua
tion
Figure 4 Flowchart of methodology
In the feature extraction stage the features of bar chartimages are used to detect plagiarism Various types of barchart images are analyzed to detect the features of imageThebar chart images inferred to acquiremaximum of ten featuresrepresenting the information and the data of image Thesefeatures are common in different types of bar chart imagesfor instance in 2D and 3D images However the number ofuses for these features may different from each other
The features extraction is an essential process to get thedata from images which can be utilized to detect the rate ofplagiarized data Therefore these ten features are categorizedinto low-level and high-level features The low-level featuresrefer to the text features of bar chart The text features are thetext which can be used in image to represent the informationand data such as caption of image label of each bar label ofcoordinates and values on coordinates Generally they areextracted from bar chart images using OCR tool Converselythe high-level features referring to numeric features cannot beextracted using OCR toolThe extraction of numeric featuresrequires a relationship between the text and graphic compo-nents The numeric features include values of bars in imageEach bar in an image has three numeric features which canbe extracted by the proposed technique depending on StartEnd and Exact values The Start and End values representthe first and last values while the Exact one corresponds tothe real value of the bar For instance the text features forimage in Figure 5 are ldquoFigure 10 Revision Design GalleriesrdquoldquoSilver Platinum Cashrdquo and ldquo0 5 40rdquo whichrepresent caption of image and label of coordinates X and Yrespectively Meanwhile the numeric features represent thevalue of each bar which can be detected by Start End andExact features For example the Start End and Exact featuresof bar Silver are 5 10 and 6 respectively
These features are used to detect the proportion of pla-giarism for bar chart imageThe extraction of Start End and
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
6 The Scientific World Journal
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40Revision Design Galleries ()
(a)
MinMean
Std
Rosenbrock function on 100 dimensions BSFP-PSO-CUI BSFP-PSO-CAI
250E + 07
200E + 07
150E + 07
100E + 07
500E + 06
000E + 00
(b)
Figure 5 Samples of data set [17 36]
Exact values necessitates preprocessing of bar chart image tothe adjacent coordinates of the image Image scanning is thenperformed to detect the length of each bar in order to findthe numeric features for each bar Storing of the features indatabases depends on the type of features whether numericor text The features which are extracted by the proposedtechnique are represented as vectors while the text featuresthat are extracted by OCR are characterized as string
The detection methods for text plagiarism are mainlycategorized based on character semantics structure cita-tion cluster cross language and syntax Comparatively thesmaller number of textual components than normal para-graph text allows us to use character-basedmethods to detectplagiarism of bar chart imagesThe character-based methodsdepend on character matching approaches to exactly orpartially detect the identical string for features of bar chartimages Various algorithms of plagiarism are adopted in thetext as character 119899-gram to identify the similarity betweentwo strings based on the number of identical characters offeatures Some researchers use 8-gramand 5-gram techniques[34 35] for matching strings to detect plagiarismWe used 2-gram technique to detect plagiarism of bar chart imagesThistechnique is used to represent the text features of bar chartimages Different similarity measures can be used to obtainthe similarity for numeric features such as Euclidean distanceJaccard or cosine coefficient The Euclidean distance iscalculated by the following
Ec (119909 119910) =radic
sum
119894
1003816
1003816
1003816
1003816
119909
119894minus 119910
119894
1003816
1003816
1003816
1003816
2 (1)
Once the detection and storing of the proportion ofplagiarism are completed then the performance of the systemis evaluated The performance is evaluated by overlapping of
features using the relation of Precision andRecall given by thefollowing
Recall = Relavent Documents RetreivedAll Relavent Documents
Precision = Relavent Documents RetreivedAll Documents Retrived
(2)
5 Experimental Results
The bar chart plagiarism detections are carried out in fourmain stages such as submission of query images featureextraction of bar chart image plagiarism detection andhighlighting results The first stage is to submit varioustypes of query images covering different kinds of possibleplagiarism to detect and judge plagiarism of bar chart imageswhile the features of query are extracted in the second stageThe third stage includes detection of plagiarism by usingword 2-gram and Euclidean distance techniques Finally thefeatures of query bar chart image that are plagiarized fromothers are highlighted and the proportion of the similarity isdisplayed
The bar chart plagiarism is further divided into ExactCopy and Modified Copy as explained in taxonomyFigure 6(a) shows the query image while Figure 6(b)depicts the plagiarized images detected by the system Thefirst plagiarized image is similar to the whole data in theimage while the second plagiarized image contains the samedata that was plagiarized but presented as a horizontal barchart image The system extracts the features of query imageand detects the proportion of plagiarism depending on StartEnd and Exact values for each bar as well as the label of eachbar The system highlights the data and information that areplagiarized and provides the proportion of plagiarism
One of the patterns of plagiarism derived from ModifiedCopy is the stealing by changing scales Each bar is modified
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World Journal 7
0
10
20
30
40
Cash Bonds Stocks Gold SilverPlatinumFigure Revision Design Galleries
Query image
()
(a)
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]Gold [0 10 10]Platinum [0 10 6]Silver [0 10 5]
Figure Revision Design Galleries
The rate of similarity is 100
Cash [30 40 32]Bonds [20 30 22]Stocks [20 30 22]
Gold [0 10 10]Platinum [0 10 6]
Silver [0 10 5]Figure Revision Design Galleries
Cash Bonds Stocks Gold SilverPlatinum
Figure Revision Design Galleries ()
0
10
20
30
40
()
Cash
Platinum
BondsStocks
Gold
Silver
0 5 10 15 20 25 30 35 40
The rate of similarity is 71
Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]
Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
The rate of similarity is 71Cash [30 - 32]Bonds [20 - 22]Stocks [20 - 22]Gold [- - 10]Platinum [- 10 6]Silver [- 10 5]
Figure Revision Design Galleries
Plagiarized image
Plagiarized image
Figure Revision Design Galleries
(b)
Figure 6 Plagiarism detection for one of Exact Copy patterns as plagiarism of the whole data of image
by plagiarists by changing Start and End values to be differentfrom the original image Figure 7 illustrates the significantrole played by the Exact values to detect this type of plagia-rism
The plagiarists may use integration among patterns ofpossible bar chart plagiarism to present a more compleximage which has the same data quoting from other worksFigure 8 displays the query image which is modified bychanging colours and scales of bars as well as changing theirlocation via swapping The proposed system is capable ofdetecting this type of plagiarism and identifies the proportionof similarity
Figure 9 illustrates the performance of the system for pla-giarism detection of Exact Copy andModified Copy patternsrespectively
6 Discussion
The state-of-the-art graphical plagiarism techniques andpatterns are presentedThe graphical plagiarism is consideredone of the electronic crimes and thefts and the concepts ofsuch stealing are newly viewed Various important informa-tion and data can be represented as graphical forms suchas results or frameworks for academic and business aspectsHowever many systems of text plagiarism methods such asTurnitin are incapable of detecting plagiarism of images Inspite of the different styles of bar chart images the extractionof features of image plays an important role in detectingplagiarism Our proposed technique which is used to extractthe numeric features played an essential role for bar chartplagiarism detection The patterns of Exact Copy of bar chart
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
8 The Scientific World Journal
Query image
TA fo
und
40
32
24
16
8
0CTN CEA PP CA TPST
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(a)
40
35
30
25
5
0CTN CEA PP CA TPST
20
15
10
TA fo
und
Plagiarized image
The rate of similarity is 53 CTN-manual search [- 40 36]CTN-system search [- - 35]
CEA-system search [- - 29]PP-manual search [- - 32]
CA-manual search [- - 32]
TPST-manual search [- - 8]TPST-system search [- - 6]
PP-system search [- - 32]
CA-system search [- - 32]
CEA-manual search [- - 29]
Comparison between manual search and system search from UK e-commerce website
Manual searchSystem search
Trust attributesComparison between manual search and system search
from UK e-commerce website
(b)
Figure 7 Plagiarism detection for one of the Modified Copy patterns which is the stealing by changing scales of image
plagiarism detection including direct copy of all data orpart of data are underscored Plagiarism which is carriedout by modifying caption of images via restructuring orsummarizing for label sentences is emphasized Alternativelythe patterns of Modified Copy which are regarded as morecomplicated than Exact Copy patterns are also analyzedThe difficulty of these patterns is the changing on imagewhich appears as the same data and information in differentformsThe restructuring of information for image within thesame shape is also covered The edition of bar chart imagesincluding the change of image bar colors or changing thebar locations either by swapping or via generating horizontalbars from vertical bars and vice versa is discussed in detail
Besides more professional modification such as changingof scales on coordinates which is completely different fromoriginal one can be detected by the proposed method Inthis case the Start and End features of bars are completelydifferent Consequently the Exact features as well as otherattributes play significant role in detecting plagiarism in barchart image
7 Conclusion
We demonstrate the precise recognition of different plagia-rized patterns in business documents using an intelligent barchart detection system The types and patterns of plagiarism
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World Journal 9
Query image
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 8 16 24 32 40 Example of bar chart Revision redesigns ()
(a)
Example Revision redesigns for input bar charts ()
Orthoptera
Hymenoptera
Hemiptera
Lepidoptera
Other orders
Coleoptera
0 10 20 30 40
The rate of similarity is 46
Orthoptera [- - 9]Hymenoptera [- - 11]Hemiptera [- - 11]Lepidoptera [0 - 7]Other orders [- - -]Coleoptera [- - -]
Plagiarized image
(b)
Figure 8 Plagiarism detection for integration among possible bar chart plagiarism
1
08
06
04
02
0
Prec
ision
1080604020Recall
(a)
1
08
06
04
02
0
Prec
ision
1080604020Recall
(b)
Figure 9 The evaluation of performance (a) for Exact Copy patterns and (b) forModified Copy patterns
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
10 The Scientific World Journal
are presented via taxonomy of figure chart and table Variouskinds of possible plagiarism are highlighted using taxonomyPlagiarism of bar chart image as type of chart is detectedby the newly proposed technique It is established that thepresent technique is capable of extracting the features from abar chart image which cannot be pulled out using OCR toolOur technique first recognizes the connection between thetext and graphical components to extract the Start End andExact value for each bar Using word 2-gram and Euclideandistance methods the accurate detection of plagiarism isperformed The detection of plagiarism is based on tenstriking features The system is capable of detecting differentlevels of plagiarism not only copy and paste of bar chartimage but also modification on images such as changingcolor or scales The present system efficiently and accuratelydistinguishes other possible alteration administered on theseimages such as swapping among bars location and evenchanges on caption via summarizing and restructuring Theproposed technique may be useful for intelligent plagiarismdetection in business and academic documents
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The authors extend their appreciation to the Deanship ofScientific Research at King Saud University for funding thiswork through Research Group no RGP-264 The authorsare also thankful to Ministry of Science and TechnologyInnovation (MOSTI) Malaysia and Research ManagementCenter (RMC) Universiti Teknologi Malaysia (UTM) JohorMalaysia for their technical support and expertise in con-ducting this research
References
[1] A M El Tahir Ali H M D Abdulla and V Snasel ldquoSurveyof plagiarism detection methodsrdquo in Proceedings of the 5th AsiaModelling Symposium (AMS rsquo05) pp 39ndash42 May 2011
[2] A Rehman and T Saba ldquoFeatures extraction for soccer videosemantic analysis current achievements and remaining issuesrdquoArtificial Intelligence Review vol 41 no 3 pp 451ndash461 2014
[3] A Rehman and T Saba ldquoEvaluation of artificial intelligent tech-niques to secure information in enterprisesrdquo Artificial Intelli-gence Review pp 1ndash16 2012
[4] S M Alzahrani N Salim and A Abraham ldquoUnderstandingplagiarism linguistic patterns textual features and detectionmethodsrdquo IEEE Transactions on Systems Man and CyberneticsC Applications and Reviews vol 42 no 2 pp 133ndash149 2012
[5] T Saba and A Altameem ldquoAnalysis of vision based systems todetect real time goal events in soccer videosrdquo Applied ArtificialIntelligence vol 27 no 7 pp 656ndash667 2013
[6] K J Ottenstein ldquoAn algorithmic approach to the detection andprevention of plagiarismrdquo SIGCSE Bulletin vol 8 pp 30ndash411976
[7] A Parker and J O Hamblen ldquoComputer algorithms for plagia-rismdetectionrdquo IEEETransactions on Education vol 32 pp 94ndash99 1989
[8] J P Bao J Y Shen X D Liu andQ B Song ldquoSurvey on naturallanguage text copy detectionrdquo Journal of Software vol 14 no 10pp 1753ndash1760 2003
[9] T Saba and A Rehman Machine Learning and Script Recogni-tion Lambert Academic Publisher 2012
[10] W Huang S Zong and C L Tan ldquoChart image classificationusing multiple-instance learningrdquo in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV 07) p27 Austin Tex USA February 2007
[11] T Saba A Rehman and M Elarbi-Boudihir ldquoMethods andstrategies on off-line cursive touched characters segmentationa directional reviewrdquo Artificial Intelligence Review 2011
[12] A Rehman D Mohammad G Sulong and T Saba ldquoSimpleand effective techniques for core-region detection and slantcorrection in offline script recognitionrdquo in Proceedings of theIEEE International Conference on Signal and Image ProcessingApplications (ICSIPA rsquo09) pp 15ndash20 Kuala Lumpur MalaysiaNovember 2009
[13] A Rehman and T Saba ldquoDocument skew estimation andcorrection analysis of techniques common problems andpossible solutionsrdquo Applied Artificial Intelligence vol 25 no 9pp 769ndash787 2011
[14] A Rehman and T Saba ldquoOff-line cursive script recognitioncurrent advances comparisons and remaining problemsrdquo Arti-ficial Intelligence Review vol 37 no 4 pp 261ndash288 2012
[15] T Saba A Rehman and G Sulong ldquoImproved statisticalfeatures for cursive character recognitionrdquo International Journalof Innovative Computing Information and Control vol 7 no 9pp 5211ndash5224 2011
[16] T Helmy ldquoA computational model for context-based imagecategorization and descriptionrdquo International Journal of Imageand Graphics vol 12 no 1 Article ID 1250001 2012
[17] M Savva N Kong A Chhajta F-F Li M Agrawala and JHeer ldquoReVision automated classification analysis and redesignof chart imagesrdquo in Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology (UISTrsquo11) pp 393ndash402 Santa Barbara Calif USA October 2011
[18] S Elzer S Carberry I Zukerman D Chester N Green and SDemir ldquoA probabilistic framework for recognizing intention ininformation graphicsrdquo in Proceedings of the 19th InternationalJoint Conference on Artificial Intelligence (IJCAI rsquo05) pp 1042ndash1047 Edinburgh UK August 2005
[19] S Carberry S Elzer N Green K McCoy and D ChesterldquoUnderstanding information graphics a discourse-level prob-lemrdquo in Proceedings of the 4th SIGdial Workshop of DiscourseandDialogue (SIGDIAL rsquo03) pp 1ndash12 Sapporo Japan July 2003
[20] M S M Rahim A Rehman S Nirsquomatus F Kurniawan andT Saba ldquoRegion-based features extraction in ear biometricsrdquoInternational Journal of Academic Research vol 4 no 1 pp 37ndash42 2012
[21] N Yokokura and T Watanabe ldquoLayout-based approach forextracting constructive elements of bar-chartsrdquo in Graph-ics Recognition Algorithms and Systems K Tombre and AChhabra Eds vol 1389 pp 163ndash174 Springer Berlin Germany1998
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World Journal 11
[22] Y Zhou and C L Tan ldquoLearning-based scientific chart recog-nitionrdquo in Proceedings of the 4th IAPR International Workshopon Graphics Recognition (GREC rsquo01) pp 482ndash492 2001
[23] Y P Zhou and C L Tan ldquoHough technique for bar chartsdetection and recognition in document imagesrdquo in Proceedingof the International Conference on Image Processing (ICIP 00)vol 2 pp 605ndash608 Vancouver Canada September 2000
[24] W Huang C L Tan and W K Leow ldquoModel-based chartimage recognitionrdquo in Graphics Recognition Recent Advancesand Perspectives J Llados and Y-B Kwon Eds vol 3088 pp87ndash99 Springer Berlin Germany 2004
[25] A Mishchenko and N Vassilieva ldquoModel-based chart imageclassificationrdquo in Advances in Visual Computing G Bebis RBoyle B Parvin et al Eds vol 6939 of Lecture Notes inComputer Science pp 476ndash485 Springer Berlin Germany 2011
[26] W Huang and C L Tan ldquoA system for understanding imagedinfographics and its applicationsrdquo in Proceedings of the ACMSymposium on Document Engineering pp 9ndash18 ManitobaCanada August 2007
[27] M M Hassan and W Al Khatib ldquoSimilarity searching instatistical figures based on extracted meta datardquo in Proceedingsof the ComputerGraphics Imaging andVisualisation (CGIV 07)pp 329ndash334 Bangkok Thailand August 2007
[28] L Yang W Huang and C L Tan ldquoSemi-automatic groundtruth generation for chart image recognitionrdquo in Proceedings ofthe 7th international conference on Document Analysis SystemsNelson New Zealand 2006
[29] G D Tourassi ldquoJourney toward computer-aided diagnosis roleof image texture analysisrdquo Radiology vol 213 no 2 pp 317ndash3201999
[30] D A Chandy J S Johnson and S E Selvan ldquoTexture featureextraction using gray level statistical matrix for content-basedmammogram retrievalrdquoMultimedia Tools and Applications vol72 no 2 pp 2011ndash2024 2014
[31] F Han HWang B Song et al ldquoA new 3D texture feature basedcomputer-aided diagnosis approach to differentiate pulmonarynodulesrdquo in Medical Imaging Computer-Aided Diagnosis Pro-ceedings of SPIE San Diego Calif USA 2014
[32] F Han H Wang B Song et al ldquoEfficient 3D texture featureextraction from CT images for computer-aided diagnosis ofpulmonary nodulesrdquo in Proceedings of the SPIE Medical Imag-ing Computer-Aided Diagnosis vol 9035 pp 1ndash7 2014
[33] S M Eissen B Stein and M Kulig ldquoPlagiarism detectionwithout reference collectionsrdquo in Advances in Data AnalysisR Decker and H-J Lenz Eds pp 359ndash366 Springer BerlinGermany 2007
[34] J Kasprzak M Brandejs and M Kripac ldquoFinding plagiarismby evaluating document similaritiesrdquo in Proceedings of the 3rdWorkshop onUncovering Plagiarism Authorship and Social Soft-ware Misuse (PAN rsquo09) pp 24ndash28 Donostia Spain September2009
[35] C Basile D Benedetto E Caglioti G Cristadoro and MD Esposti ldquoA plagiarism detection procedure in three stepsselection matches and squaresrdquo Donostia Spain 2009
[36] Z A Hamed and S Z Mohd Hashim Hybrid particle swarmoptimization and black stork foraging for functional neural fuzzynetwork learning enhancement [UTMThesis] 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014