REVERSE IMAGE SEARCH

REVERSE IMAGE SEARCHGroup 6

Group Members:Vrujal Gandhi(201103100710010)Jimit Vaidya(201103100710054)Denish Jariwala(201203100720007)Nikunj Rana(201203100720021)Guided By:Prof. Mayank KapadiaAssistant ProfessorC. G. Patel Institute of Technology11OUTLINEIntroductionMotivationObjectiveMethods/ Algorithms/ Block DiagramsResultsApplicationConclusionReferencesDemo2INTRODUCTIONReverse Image SearchSearching an Image by using a query Image, rather than the text.

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3MOTIVATION4We can find similar, relevant and unknown images by using Reverse Image Search.

Object-1Object-2Object-3OBJECTIVEEase of searching.Reduced searching time.More results based on visual similarities.

5TEXT BASED SEARCH vsIMAGE BASED SEARCH6Lookup for matching images in database Type keyword in search engineResult (Images) Input Image Extract the features and contents Look for similar features and content in image databaseResult (Similar images to query image )RIS : BLOCK DIAGRAM7Retrieved Relevant ImagesFeature VectorImage DatabaseFeature ExtractionQuery ImageFeatures DatabaseFeature Extraction of each ImageSimilarity Measuremente.g. Histogram/DWT/Edgee.g. Histogram/DWT/Edgee.g. EDTECHNIQUESColor: Global HistogramLocal Histogram

Texture:Discrete Wavelet TransformPrincipal Component Analysis

8Shape:Canny Edge DetectorSobel OperatorPrewitt Operator

GLOBAL HISTOGRAM Colour TechniqueGlobal Histogram : 9

Image Pixel valueRGB PlaneFeature = [mean2(R) mean2(G) mean2(B)]LOCAL HISTOGRAM Colour TechniqueLocal Histogram :10

Image

Part - 1

Part - 2

Part - 3Part - 4Feature = [mean2(Rn) mean2(Gn) mean2(Bn)] Where, n = number of parts10DISCRETE WAVELET TRANSFORM Texture TechniqueTo analysis texture feature using Discrete Wavelet Transform for three level is given as,

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DISCRETE WAVELET TRANSFORM Texture TechniqueOne Level of the TransformThe DWT of a signal is calculated by passing it through a series of filters. First the samples are passed through a low pass filter with impulse response resulting in a convolution of the two:

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CANNY EDGE DETECTOR Shape TechniqueThe Process of Canny edge detection algorithm can be broken down to 5 different steps:Apply Gaussian filter to smooth the image in order to remove the noiseFind the intensity gradients of the imageApply non-maximum suppression to get rid of spurious response to edge detectionApply double threshold to determine potential edgesTrack edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges.

13Performance Measurement Parameters14Similarity Measurement Parameters15

where, n = number of samplesQuery Image16

LOCAL HISTOGRAM Results17

DISCRETE WAVELET TRANSFORM Results18

CANNY EDGE DETECTOR Results19

FUSION OF THREE Results20

RESULT ANALYSIS21Query ImageLocal HistDWTCannyFusionTotal Images in DatabaseAfrican People168518632910411038753742126312558629Average~95.229.2Precision Rate~56.2532.5012.5057.50Recall Rate~14.298.253.1714.60Beach173145714891791252111801438141921041110197113811Average~12.247.212Precision Rate~76.2525.0045.0075.00Recall Rate~13.714.498.0913.48RESULT ANALYSIS22Query ImageLocal HistDWTCannyFusionTotal Images in DatabaseBuilding2241041108424210511024391192441131102489319Average~9.83.219.6Precision Rate~61.2520.006.2560.00Recall Rate~49.0016.005.0048.00Bus30211311099330145114332127112346115111368124113Average~124.8112Precision Rate~75.0030.006.2575.00Recall Rate~60.0024.005.0060.00RESULT ANALYSIS23Query ImageLocal HistDWTCannyFusionTotal Images in DatabaseDragon4011616161610043416161616454161610164661616161649716161616Average~161614.816Precision Rate~100.00100.0092.50100.00Recall Rate~80.0080.0074.0080.00Elephant51714811410052115104155291495145441484145531411114Average~14.29.2314.2Precision Rate~88.7557.5018.7588.75Recall Rate~71.0046.0015.0071.00RESULT ANALYSIS24Query ImageLocal HistDWTCannyFusionTotal Images in DatabaseFlower6001545151006111511515627144414636165316644165616Average~15.25.84.615.2Precision Rate~95.0036.2528.7595.00Recall Rate~76.0029.0023.0076.00Horse7001541151007011621016702166141677415315157911661316Average~15.64.210.615.6Precision Rate~97.5026.2566.2597.50Recall Rate~78.0021.0053.0078.00RESULT ANALYSIS25Query ImageLocal HistDWTCannyFusionTotal Images in DatabaseMountain816115911958181357138291361113843123412852131513Average~12.447.212.4Precision Rate~77.5025.0045.0077.50Recall Rate~62.0020.0036.0062.00Food9371311013100942141114950134212974134713977133713Average~13.202.605.4013.00Precision Rate~82.5016.2533.7581.25Recall Rate~66.0013.0027.0065.00APPLICATIONSDetect copyright violationsTrack images on the internetSearch relevant content instantlyForensic Lab ResearchBio-Medical ResearchUnknown Entities Identification

26Architectural and Engineering DesignArt CollectionCrime PreventionPhotograph AchievesNudity-Detection FiltersFace Recognition and Finding

And lots more...CONCLUSIONReverse Image Search (RIS) performs well as all three feature extraction techniques are used together. Whichever feature extraction technique we use has certain drawbacks and to cover them up we used the fusion of all three feature extraction technique. It shows the relevant retrieved images as per the query image with more accuracy by using more advanced algorithm and reduces time taken by loading pre-defined image database in the code.27REFERENCESD.Jeyabharathi and Dr.A. Suruliandi, Performance Analysis of Feature Extraction and Classification Techniques in CBIR, 2013 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2013]

Amandeep Khokher and Rajneesh Talwar, Content-based Image Retrieval: Feature Extraction Techniques and Applications, International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012)

Bouden Toufik and Nibouche Mokhtar, The Wavelet Transform for Image Processing Applications, Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, Dr. Dumitru Baleanu (Ed.), ISBN: 978-953-51-0494-0, InTech (2012)

Amanbir Sandhu and Aarti Kochhar, Content Based Image Retrieval using Texture, Color and Shape for Image Analysis International Journal of Computers & Technology ISSN: 2277-3061 Volume 3, No. 1, AUG, 2012

28REFERENCESMs. K. Arthi and Mr. J. Vijayaraghavan, Content Based Image Retrieval Algorithm Using Colour Models International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 3, March 2013

Ahmed J. Afifi and Wesam M. Ashour, "Content-Based Image Retrieval Using Invariant Color and Texture Features" Digital Image Computing Techniques and Applications (DICTA), 2012 International Conference on, At Fremantle, Western Australia

Dandge S.S. And Bodkhe A.P., "Content Based Image Retrieval System" Journal of Signal and Image Processing, Volume 3, Issue 2, 2012

Jun Yue, Zhenbo Li, Lu Liu and Zetian Fub,"Content-based image retrieval using color and texture fused features" Mathematical and Computer Modelling, Volume 54, Issues 34, August 2011

29REFERENCESNidhi Singh,Kanchan Singh and Ashok K. Sinha,"A Novel Approach for Content Based Image Retrieval" 2nd International Conference on Computer, Communication, Control and Information Technology( C3IT-2012) on February 25 - 26, 2012

Shankar M. Patil, "A Content Based Image Retrieval using color, texture and shape", International Journal of Computer Science and Engineering Technology", Vol. 3 , No. 9 ,pp. 404-410, 2012.

Irena Valova and Boris Rachev,"A Content Based Image Retrieval System Based on Color Features" November 2004, CODATA, Berlin Germany

30DEMO and Q & ATHANK YOU ! 31