Strategic Approach to Image Block Segmentation

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 10 pp 604-609 January 2014 www.ijsret.org ISSN 2278 0882

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Strategic Approach to Image Block SegmentationAvanish Shrivastava, Prof.Mohan Awasathy

Department of Electronics and Telecommunication Engineering, SSCET, Bhilai,Department of Electrical and Electronics Engineering, Institute of Technology, Korba.

CSVTU, Bhilai, Chhattisgarh, India

AbstractImage segmentation is the problem ofpartitioning an image into its constituent components.Image segmentation being an ill-defined problemthereis no unique ground-truth segmentation of an image,against which the output of an algorithm may becompared. However, many segmentation algorithms havea parameter that explicitly encodes the number of clustersand yet, do not have well accepted schemes for itsselection. Choosing the appropriate segmentation scalesfor distinct ground objects and intelligently combiningthem together are two crucial issues to get the appropriatesegmentation result for target applications. We propose ablock-based method of segmentation that is capable ofdealing with intensities of blocks and processing any oneblock at time, out of many blocks for the analysis ofbackground variations, illumination and comparing thoseblocks on the basis of their intensities. This approachleads to an issue of choosing block sizes. For block-basedclassification, an image is divided into blocks, and afeature is detected for each block by grouping statisticsextracted from the block. Specifically, images areanalysed on a non-overlapping block-by-block basis. Thestandard deviation or mean or average of the pixelintensities of the block can be used as features. It is takenas a primary feature, for identifying background blocks.Features and algorithms for the block-segmentation andclassification of regions into respective block areevaluated for applicability to grayscale images only.

Keywords Segmentation, DCT, tracking, Blockprocessing.

I. INTRODUCTIONOne of the fundamental and critical tasks in manycomputer-vision applications is the segmentation offoreground objects of interest from an image sequence.Many image processing tasks require to know themeaning (e.g. object or background) of the image pixels.Image segmentation is an important process to furnishsuch information to many image processing applicationssuch as pattern recognition and object identification.Image segmentation is a process of dividing an imageinto different regions such that each region is nearlyhomogeneous, whereas the union of any two regions isnot (Unnikrishnan R. et. Al. 2007). It serves as a key inimage analysis and pattern recognition and is afundamental step toward low-level vision, which is

significant for object recognition and tracking, imageretrieval, face detection, and other computer-vision-related applications. Note that rather than merelyproviding a labelling of all regions in the image, thesegmentation process must extract the object of interestfrom the background to support the subsequent featureextraction and object classification processing. Theaccuracy of segmentation can significantly affect theoverall performance of the application employing itsubsequent processing stages use only the foregroundpixels rather than the entire frame. The traditionalprocessing flow for image-based pattern recognitionconsists of image segmentation followed by classification.This approach assumes that the segmentation is able toaccurately extract the object of interest from thebackground image autonomously. The performance ofthis subsequent processing is strongly dependent on thequality of this initial segmentation. This expectation ofideal segmentation is rather unrealistic in the absence ofany contextual information on what object is beingextracted from the scene.

Previous Work on Segmentation

Various segmentation methodologies developedpreviously have been glanced in this paper. Though theyall perform the segmentation operation but they differdepending upon the application they are meant for. Hencethe different segmentation process are applicable torespective application only, if they are applied to otherones, the extent of error addition is unexpectedly highincluding the changes in the image which vanishes theirknowledgeablity. These changes in the image can becaused by variations in the object itself (i.e., differentcolor or texture), or by variations in the environmentalfactors, such as the sensor, lighting conditions, and mostimportantly shadow and highlight bands which causenonuniform changes in the appearance of the objects.Again since the existing methods require homogeneity ofthe object of interest, any nonuniform changes will leadto a violation of the homogeneity assumption.Segmentation is employed in diverse applications, suchas tracking ( Xiaofeng Liu, 2010), action recognition(Johnson Andrew E. and Hebert M, 2005), gaitrecognition (Wenjing Li et. Al. 2008), anomaly detection(Lina Yi et.Al. 2010), content based video coding(Xiaofeng Liu, 2010) and computational photography


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(Peijun Li et. Al. 2011).There is an abundance ofliterature on image segmentation. Methods also havebeen defined for post processing the low-levelsegmentation to further regularize the segmentationoutput. Many approaches have been proposed before,including the clustering method (Doung G. And Xie M.2005), active contours (Ping Han Lee, 2011), normalizedcuts (Tao W. and Jin H. 2007), graph-cut-basedapproaches (TaoW. and Jin H. 2007)etc.The segmentedregions arerepresented by using the graph structures, andthe N-cut method is applied to perform globallyoptimized clustering. Because the number of thesegmented regions is much smaller than that of the imagepixels, the method allows a low-dimensional imageclustering with significant reduction of the complexitycompared to conventional graph partitioning methods thatare directly applied to the image pixels. Whileconsidering every region as only one graph node there aresome inappropriate partitioning, so an improvedsegmentation strategy is needed.Graph-based approach visual group is based on severalkey factors such as similarity, proximity, andcontinuation (Brejl M. And Sonka M. 2000). A graph ispartitioned into multiple components that minimize somecost function of the vertices in the components or theboundaries between those components. So far, severalgraph cut-based methods have been developed for imagesegmentations i.e. (Tao W. and Jin H. 2007) proposed ageneral image segmentation approach based onnormalized cut (N-cut) by solving an eigensystem anddeveloped an image-partitioning approach by using acomplicated graph reduction. Besides graph-basedapproaches, there are also some other types of imagesegmentation approaches that mix the feature and spatialinformation (Lina Yi et. Al. 2010). These methods arebasically data-driven approaches. The data-driven methodsometimes fails to produce satisfactory segmentationwhen there are shadows, occlusion, cluttering, lowcontrast area, or noise in the image. Incorporation ofcontextual and prior information is very important toimprove segmentation under such situations.The paper (Tao W. and Jin H. 2007) concerns an N-cutmethod in a large scale. It has been empirically shownthat the N-cut method can robustly generate balancedclusters and is superior to other spectral graph-partitioning methods, such as average cut and averageassociation (Lee P.H. et. Al. 2011). The N-cut method hasbeen applied in scene detection (Johnson A.E. and HebertM. 2005) and cluster-based image retrieval (Doung G.And Xie M. 2005). However, image segmentationapproaches based on N-cut, in general require highcomputation complexity and therefore, are not suitablefor real-time processing.Previously, it was thought that anefficient solution to this problem is to apply the graphrepresentation strategy on the regions that are derived bysome region segmentation method (Zhang L, and Ji Q,

2011). For example, (Brejl M. and Sonka M. 2000)developed an image segmentation method thatincorporates region based segmentation and graph-partitioning approaches. This method first produces a setof over-segmented regions from an image by using thewatershed algorithm, and a graph structure is then appliedto represent the relationship between these regions.Choosing the appropriate segmentation scales for distinctground objects and intelligently combining them togetherare two crucial issues to get the appropriate segmentationresult for target applications.After analysis of various methodologies of segmentationand glancing it with the requirement in this work,basically, there are mainly three limitations of existingimage segmentation algorithms. Firstly, Existingsegmentation algorithms are built upon the following twocommon underlying assumptions (i) the object of interestshould be uniform and homogeneous with respect tosome characteristic, (ii)adjacent regions should bediffering significantly. These assumptions, however, arerarely met in real-world applications. Secondly, there arefew metrics available for evaluating segmentationalgorithms. Some of the proposed measures ofsegmentation quality include: edge-border coincidence,boundary consistency, pixel classification, object overlapand object contrast. However, none of these metrics canbe passed as to be widely accepted as ideal and manyrequire ground truth information. Some methods haveused multiple hand-segmentations from a number ofhuman experts to define a segmentation quality metricthat is really measuring the segmentation consistency(Unnikrishnan R. et. Al. 2007). This metric would clearlynot be appropriate for any real-time application and alsorequires a considerable offline effort. Thirdly, the finallimitation of existing segmentation algorithms is theirinability to adapt to real-world changes.

II. PROPOSED IMAGE BLOCK SEGMENTATIONThe proposed technique has following main components.

1. Traininga. Divide testing image into non-

overlapping square blocks with equalsize and detect the texture of the imageseparately.

b. Extract a feature (standard deviation) foreach block.

c. Estimate model parameters based on thefeature vector and their hand labelledclasses.

d. Assign each block a unique individualnumber.

2. Testinga. Compare the block segmented image

with texture bounded image.


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b. Blocks are mapped with textures ofimage.

c. Unique block numbers involved in adetected texture is given out.

This new algorithm is proposed for segmentingimages by classification of status of pixels within theblocks. The general procedure for block-basedsegmentation and classification partitions an image intonon-overlapping nn blocks. Block- segmentation andclassification algorithms can be based on the informationprovided by a multidimensional feature space.Algorithms can be improved by context-basedclassification supplementing first pass classification fromfeature information. The pixels of blocks are handled inblock by block manner, as illustrated in figure. A block ofpixels is generally defined as (qq) array of pixels. Theproposed segmentation algorithm decides one of thefollowing three possible outcomes for each block ofpixels.

(i) All the pixels within the blocks are classified asobject,

(ii) All the pixels within the blocks are classified asbackground,

(iii) No change to pixel classification.

The arrangement of block used in this algorithm is justone of many possible choices. The choice of arrangementmay have influence on the segmented image. Howeverthe segmented images using different arrangement ofblocks are expected to be similar to each other becauseeach block is still tested for change of class.There arethree possible actions on the block viz.

(i) All the pixels changed to object.(ii) All the pixels changed to background.(iii) No change is made.

Issue of Block size

For block-based image classification algorithms, imagesare divided into blocks, and decisions are madeindependently for the class of each block. This approachleads to an issue of choosing block sizes. In thisalgorithm the size of blocks of pixels is an adjustableparameter. In order to investigate the effect of block sizeon the segmented image, simulation studies are carriedout on real images. A block of pixels is denoted as a qqmatrix. Three different block sizes have been investigated.We dont want to choose a block size too large since thisobviously entails crude classification. On the other hand,if we choose a small block size, only very local propertiesbelonging to the small block are examined inclassification. The penalty then comes from losinginformation about surrounding regions. A known methodin signal processing to attack this type of problem is touse context information (Brejl M. and Sonka M. 2000).

Here the image is firstly analysed on account of the pixelcontent and safe marginal limit of block size such that theblocks shouldnt lose their unique knowledgeableinformation, hence in this way both the block sizes andclassification rules can vary according to context.The next step towards the goal is to detect out the featuresof block. Here, two methods are analysed and concludedwether to use standard deviation or the mean operation onpixels intensities of individual blocks. The individualmain features of these methods are that even afteroperation their effect on pixel remains knowledgeable,even in the worst situations. Secondly they give thesufficient information about the region of object,occupying the image space including the safe limits ofobject border issue. The standard deviation of pixelintensities () in an image block is a feasible feature forclassification of scanned images. It can be taken as aprimary feature for identifying background blocks; isvery small for background images. As cannot be usedto effectively discriminate pictures from text in postalimages. The related features variance and absolutedeviation of pixel intensities have also been mentioned asclassification features and should perform similarly to .The mean of pixel intensities () in an image block is auseful feature for classification, since it gives theknowledge of average variation of pixel intensities. Thethree classes, background, picture, and text, can beseparated into three bands based on . However, non-white backgrounds and light pictures may causemisclassification. The feature is a good supplementaltool in classification, but should not be taken as primarybecause of the variety of envelope background colors.Visual inspection will be employed to evaluate thegoodness of segmented image. A good segmented imageis one that retains as many details of image as possibleand viewer will still be able to recognize the objectcontained in image.

III. EXPERIMENTSEach image is split into blocks which are considerablysmaller than the size of the image (e.g. 22, 44 . . .1616) with each non-overlapping block with itsneighbours in both the horizontal and vertical directions.In some cases block overlapping is preferred where eachblock is overlapping its neighbours by a configurableamount of pixels (e.g., 1, 2 . . . 8) in both the horizontaland vertical directions. Block overlapping can also beinterpreted as block advancement. The most obviousdifference between the results of images with differentblock size is the increase in number of black dots as blocksize decreases.


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Fig. 1 As an investigation to the performance ofproposed segmentation algorithm, the above is the

selected image to be segmented.(Source www.mathworks.com )

A(1,1)

A(2,1) A(3,1) A(M,1)

A(1,2)

A(2,2) A(3,2) A(M,2)

A(1,3)

A(2,3) A(3,3) A(M,3)

A(1,N)

A(2,N)

A(3,N)

A(M,N)

Fig.2 Co-ordinates of pixels in digital image

Fig.3 Test Image after finding the standard deviation ofpixel intensities of blocks after block segmentation.

Fig.4 Test Image after scaling and before blocksegmentation.

Fig.5 Test Image after scaling and block segmentation.

Fig.6 Test Image after scaling and block segmentationfor k = 2.

Fig.7 Test Image after scaling and block segmentation fork = 5.

Fig.8 Test Image after scaling and block segmentationfor k = 10


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When block size is large the segmentation resultsresemble the true scene more. However the segmentedimage will have staircase like edges. As the block sizedecreases, the edge becomes smoother. Visualinspections will be employed to evaluate the goodness ofsegmented images. A good segmented image is one thatretain as much details of image as possible and viewerwill still be able to recognize the object contained inimage. For a digital image with height of N pixels and awidth of M pixels, the spatial coordinates are arranged asshown in figure. The co-ordinates of pixels are organizedsuch that the vertical position is indexed from top tobottom in terms of n, n = 1, 2.. N. The horizontalposition is indeed from left to right in terms of m, m = 1,2.M. A pixel in image is denoted as A(m, n). It isnoticed that q = 1 is a special case where one pixel isprocessed at a time. To decide the dimensions of theblock we introduce a scalar k which is helpful indeciding block size without overlapping the other blocksof same image. It is the maximum row and columndimension for the block. By changing the values of kfrom k = 1 to k = 40, the images are observed andcompared with input image of fig.1. From aboveexperiments we conclude that for the minimum value of kthe image can be understood after finding standarddeviation. As we proceed further by increasing the value,image becomes foggier.

IV. APPLICATION

A. Block FeaturesChoosing features is a critical issue in classificationbecause features often set the limits of classificationperformance. The intra-block features are definedaccording to the pixel intensities in a block. They aim atdescribing the statistical properties of the block. Featuresselected vary greatly for different applications. Widelyused examples include moments in the spatial domain orfrequency domain and coefficients of transformations,e.g., the discrete cosine transform (DCT). The inter-blockfeatures are defined to represent relations between twoblocks, for example, the difference between the averageintensities of the two blocks. The use of the inter-blockfeatures is similar to that of delta and accelerationcoefficients in speech recognition, in which there isample empirical justification for the inclusion of thesefeatures.B. Image Segmentation of DocumentsThe second application of the algorithm is thesegmentation of document images into text andphotograph. Photograph refers to continuous-tone imagessuch as scanned pictures, and text refers to normal text,tables, and artificial graphs generated by computersoftware. We refer to the normal text as text forsimplicity if the meaning is clear from context. Imagesexperimented with are 8 bits/pixel gray-scale images.This type of classification is useful in a printing processfor separately rendering different local image types. It isalso a tool for efficient extraction of data from imagedatabases.C. Comparing the BlocksA second area of interest is to extend the use of thealgorithm for block comparison among various regions todetect the intensity variations by including texturefeatures. This would be useful for other applications.Another area of interest is to explore the integration ofmore powerful low-level segmentation algorithms.

V. CONCLUSIONSIn our proposed method the images were divided intoblocks, and standard deviation over the pixels of blockwas operated. The reason to use standard deviation is tominimize the intensity variations of pixels of same blockto an extent as minimum as possible. Pixel-basedprocessing approaches to foreground detection can besusceptible to noise, illumination variations, and dynamicbackgrounds, partly due to not taking into account richcontextual information. In contrast, region-basedapproaches mitigated the effect of above phenomena butsuffered from blockiness artifacts. The proposeddetection method belonged to a region-based category,but at the same time was able to segment smoothcontours of foreground objects.


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AKNOWLEDGEMENTI gratefully acknowledge the unmatchable, helpfulknowledgeable kind comments of Prof.MohanAwasthifor improving the clarity of the paper and crystal clearingthe work.

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