ISSN: 2278 – 1323                      International Journal of Advanced Research in Computer Engineering & Technology    ...
ISSN: 2278 – 1323                    International Journal of Advanced Research in Computer Engineering & Technology      ...
ISSN: 2278 – 1323                   International Journal of Advanced Research in Computer Engineering & Technology       ...
ISSN: 2278 – 1323                       International Journal of Advanced Research in Computer Engineering & Technology   ...
ISSN: 2278 – 1323                   International Journal of Advanced Research in Computer Engineering & Technology       ...
ISSN: 2278 – 1323                              International Journal of Advanced Research in Computer Engineering & Techno...
ISSN: 2278 – 1323                             International Journal of Advanced Research in Computer Engineering & Technol...
ISSN: 2278 – 1323                    International Journal of Advanced Research in Computer Engineering & Technology      ...
ISSN: 2278 – 1323                 International Journal of Advanced Research in Computer Engineering & Technology         ...
ISSN: 2278 – 1323                  International Journal of Advanced Research in Computer Engineering & Technology        ...
ISSN: 2278 – 1323                 International Journal of Advanced Research in Computer Engineering & Technology         ...
ISSN: 2278 – 1323                       International Journal of Advanced Research in Computer Engineering & Technology   ...
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  1. 1. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Color Image Segmentationusing Clustering Technique Patel Janak kumar Baldevbhai, R.S. Anand the literature, it is observed that different transforms are used Abstract—This work presents image segmentation technique to extract desired information from remote-sensing images orbased on colour features with K-means clustering algorithm. In biomedical images (Mehmet Nadir Kurnaz et al; 2005).this we did not used any training data. In this paper, we present Segmentation evaluation techniques can be generally divideda simple and efficient implementation of k-means clusteringalgorithm. The regions are grouped into a set of classes using into two categories (supervised and unsupervised). The firstK-means clustering algorithm. Results are grouped into clusters category is not applicable to remote sensing because anso avoiding feature calculation for every pixel in the image. optimum segmentation (ground truth segmentation) isAlthough the colour is not frequently used for image difficult to obtain. Moreover, available segmentationsegmentation, it gives a high discriminative power of regions evaluation techniques have not been thoroughly tested forpresent in the image. Here clusters are grouped & segmentation remotely sensed data. Therefore, for comparison purposes, itis obtained in form of colors through which important objectsare segmented, extracted or recognized. is possible to proceed with the classification process and then indirectly assess the segmentation process through the Index Terms—color Image segmentation, K-means, clusters, produced classification accuracies. (Ahmed Darwish, et al;unsupervised classification. 2003).Clustering is a mathematical tool that attempts to discover structures or certain patterns in a data set, where the objects inside each cluster show a certain degree of I. INTRODUCTION similarity. he process of image segmentation is defined as: “theT search for homogenous regions in an image and later theclassification of these regions”. It also means the partitioning For image segment based classification, the images that need to be classified are segmented into many homogeneous areas with similar spectrum informationof an image into meaningful regions based on homogeneity firstly, and the image segments‟ features are extracted basedor heterogeneity criteria (Haralick et al; 1992). Image on the specific requirements of ground features classification.segmentation techniques can be differentiated into the The colour homogeneity is based on the standard deviation offollowing basic concepts: pixel oriented, Contour-oriented, the spectral colours, while the shape homogeneity is based onregion-oriented, model- oriented, colour oriented and hybrid. the compactness and smoothness of shape. There are twoColour segmentation of image is a crucial operation in image principles in the iteration of parameters:1) In addition toanalysis and in many computer vision, image interpretation, necessary fineness, we should choose a scale value as large asand pattern recognition system, with applications in scientific possible to distinguish different regions; 2) we should use theand industrial field(s) such as medicine, Remote Sensing, colour criterion where possible. Because the spectralMicroscopy, content- based image and video retrieval, information is the most important in imagery data, the qualitydocument analysis, industrial automation and quality control of segmentation would be reduced in high weightiness of(Ricardo Dutra, et al;2008). The performance of colour shape criterion.segmentation may significantly affect the quality of an image This work presents a novel image segmentation based onunderstanding system (H.S.Chen et al; 2006).The most colour features from the images. In this we did not used anycommon features used in image segmentation include training data and the work is divided into two stages. Firsttexture, shape, grey level intensity, and colour. The enhancing color separation of satellite image using decorconstitution of the right data space is a common problem in relation stretching is carried out and then the regions areconnection with segmentation/classification. In order to grouped into a set of five classes using K-means clusteringconstruct realistic classifiers, the features that are sufficiently algorithm. Using this two-step process, it is possible torepresentative of the physical process must be searched. In reduce the computational cost avoiding feature calculation for every pixel in the image. Although the colour is not Manuscript received June 19, 2012. frequently used for image segmentation, it gives a high Patel Janakkumar Baldevbhai is with the Image and Signal Processing discriminative power of regions present in the image.Lab., Electrical Engineering Department, Research Scholar, EED, IndianInstitute of Technology Roorkee, Uttarakhand, India on duty leave under Colour segmentation is an essential issue with regard toQIP scheme of AICTE from the L.D.R.P. Institute of Technology & Research, vision applications, such as object detection and navigationGandhinagar, and Gujarat, India. (Corresponding author phone: (Bosch et al., 2007; Lin, 2007). The process of color09458121095; 079-23221371(R) e-mail: R.S. Anand is with the Electrical Engineering Department, Professor, segmentation consists of color representation, color featureEED, Indian Institute of Technology Roorkee, Uttarakhand, India extraction, similarity measurement and classification. In 563 All Rights Reserved © 2012 IJARCET
  2. 2. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012color representation, the RGB (Red, Green and Blue) model, used to estimate the clustering index (Al Aghbari and Al-Haj,which expresses color as a mixture of red, green and blue 2006). The idea of a „histon‟, which is an encrustation of athree color components, is often used to depict the color histogram such that the elements in the histon are the set of allinformation of an image (Bascle et al., 2007; Weng et al., the pixels that can be classified as possibly belonging to the2007). By using a transformation, the secondary colors, same segment, was introduced for color segmentation bywhich are CMY (Cyan, Magenta and Yellow) or Murshrif and Ray (2008), and the total computation time thisRG–GB–BR, can be obtained and used as an alternative color approach requires for a 179X122 image is 2.41 s. Neuralmodel (Wang et al., 2007). The HSI model, which transforms networks (Bascle et al., 2007) have recently been used as aRGB into Hue, Saturation and Intensity, is also a popular clustering kernel for color segmentation, where componentscolor model at present, and its good performance has been of the RGB space and the intensity are used as inputs andshown in many works (Kim et al., 2007, 2008; Wangenheim three calibrated colour components are considered as outputset al., 2007). HSV (Value) and HSL (Luminance) are very of the modified multi-layer perceptron (MLP). After thesimilar to the HSI model due to the transformation formulas training procedure, good segmentation performance isapplied. Using the HSI color model, a specific color is able to achieved. Furthermore, the look-up tables (LUT) of thebe recognized regardless of variations in saturation and modified MLP can be applied for real-time applications, sointensity. CIE Luv, CIE Lab and YCbCr (Wang and Huang, that the execution time for a 320X 240 image is only 0.003752006; He et al., 2007) are color spaces which represent a s. However, a huge database needs to be created for thiscolor by its lightness (L), luminance (Y) and chromaticity system to work, and if an input image is very different from(uv, ab and CbCr). The idea of color ratio was first those in the database, the network should be re-trained tointroduced by Barnard and Finlayson in 2000 to identify the improve the results. The well-known K-means method„„shadow‟‟ and „„non-shadow‟‟ regions to be robust under (Lloyd) is one of the most commonly used techniques in thechanges in luminance. In 2002, the RGB ratio of the pixel clustering-based segmentation field for industrialvalue to the local sum (R/Rsum, G/Gsum, B/Bsum) was applications and machine learning (Berkhin, 2002; Mignotte,proposed by Finlayson et al. to deal with the influences of 2008). The fuzzy c-means theory (the fuzzy version ofshadows produced by variations in illumination. In addition, K-means) is applied as the clustering method (Kuo et al.,Finlayson et al. (2005) presented an alternative RGB ratio 2008), and similarity measurement is based on Euclideandefinition, which is the ratio of the intensity of a pixel to the distance (Luis-Garcia et al., 2008). Bosch et al. (2007)local average (R/Rave, G/Gave, B/Bave), and this formula is presented an approach that can recognize grass, sky, snowused due to its invariance to luminance and device changes. and road using fuzzy logic with predefined classes, for whichIn this paper, we propose a new RGB ratio model, which is the average processing time for an image size of 180X120 tobased on the fact that a change in the intensity of a reference 250X250 is 60 s. Efficient fuzzy c-means clustering (qFCM)color will lead to a change in the RGB color components, but is also applied to speed up the clustering process by splittingtheir ratios to the reference color (R/Rref, G/Gref, B/Bref) a target image into several small sub-images (Chen et al.,will be linear to an intensity change (Benedek and Sziranyi, 2005). The computation time that qFCM requires for a2007; Mikic et al., 2000). With this property, a specific color, 128X128 gray-level image is 0.1–1.2 s. The use of a templatesuch as the reference Colour, can be described as a linear image is another fast segmentation method. For instance, ancolor model, so that it is invariant to intensity variation. image database of eyes can be established, and a skin colourMoreover, information about the three color components database can be obtained from a colour conversion matrix(RGB) is used to describe the chromaticity by the proposed with color of the sclera. Consequently, fixed thresholds of theRGB ratio space. Therefore, while inheriting the HSV space are introduced to detect the skin area in an inputcharacteristics of HSI and RGB models, the RGB ratio has image (Do et al., 2007). However, the use of template imagesseveral advantages with regard object recognition under is restricted to specific objects, and may require a large imagevariations in intensity. database. In this paper, a dynamic fuzzy variable range isThere exist many complex and state-of-the-art techniques for proposed to achieve a high quality segmentation result.colour segmentation which are excellent at partitioning an Firstly, the linearity between the RGB ratio and intensity isinput image. For example, the global color statistics can be estimated by a linear progressive method and parameterrepresented by a set of overlapping regions and modeled by a estimation. Secondly, upper and lower boundaries aremixture of Gaussians (GMM), and a local mixture model is obtained statistically for each colour ratio. These boundariesdescribed by Markov Random Fields (Kato, 2008). By are used to define the fuzzy membership functions ofcoloroptimizing parameters of the global and local models, the ratio clusters, which dynamically vary corresponding tomaximum likelihood is estimated and then a pixel can be intensity changes. The proposed fuzzy system‟s parameterclassified. Although this approach has good segmentation optimization, undertaken using a back propagation neuralresults, a large number of iterations are necessary to network, makes the fuzzy decision more adaptive and moredetermine the optimal parameters. As a result, 16 s of effective. Meijer (1992) used sine-wave sounds to transformcomputation time is needed for an image with a 256X256 image information without any image pre-processing, while aresolution (Tai, 2007). Hill manipulation of the colour multi-resolution approach was introduced to image-to-soundhistogram is another widely used approach to achieve colour mapping by Capelle et al. (1998).segmentation. A three-dimensional histogram can be The present work is organized as follows: Section 2obtained by accumulating three colour components of pixels. describes the data resources and software used. Section 3Dominant hill detection and minor hill dismantling are then describes the enhancing colour separation of image using 564 All Rights Reserved © 2012 IJARCET
  3. 3. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012decor relation stretching. Section 4 describes the K-means clusters to be located in the data. The algorithm thenclustering method. In section 5 the proposed method of arbitrarily seeds or locates, that number of cluster centers insegmentation of image based on colour with K-means multidimensional measurement space. Each pixel in theclustering is presented and discussed. Experimental results image is then assigned to the cluster whose arbitrary meanobtained with suggested method are shown in section 6. vector is closest. The procedure continues until there is noFinally, section 7 concludes with some final remarks. significant change in the location of class mean vectors between successive iterations of the algorithms (Lille sand Mean shift-based clustering and Keiffer, 2000). As K-means approach is iterative, it is A clustering algorithm based on mean shift was proposed computationally intensive and hence applied only to image subareas rather than to full scenes and can be treated as in [13]. Unfortunately, it becomes impractical in the unsupervised training areas (Lillesand & Keiffer, 2000).context of texture segmentation due to the expensivecomputation required in order to find the nearest neighbours K-means-based clusteringof a point in a highdimensional space. Hence, in this work, an Due to its simplicity and good convergence properties, theapproximate version has been utilized. It starts by initializing iterative k-means algorithm is probably the most widely usedthe mean shift procedure on a given point and then iterates as clustering algorithm. However, it suffers from importantusual until a stationary point is reached. However, at each drawbacks, such as the requirement of specifying the numberiteration, all points involved in the mean shift computation of clusters and the non-deterministic results produced ifare marked as “already visited”. Therefore, they are not taken random initialization is used (which is often the case).as initial points anymore. These points are also assigned a In order to overcome the aforementioned problems, avote regarding their membership to the cluster associated wrapper for k-means, which is a variation of thewith the mode being detected. The algorithm repeats this resolution-driven clustering algorithm proposed in [11], hasprocedure with the remaining “not visited” points. been applied. It has two main stages: split and refinement. Once all mode candidates have been found, mode merging Regarding the split stage, let us assume that the data pointsis performed by means of the same approximate mean shift have been split intoalgorithm by considering the found modes as data points. If C disjoint clusters (initially C = 1). The mean distancetwo modes are merged, their membership votes are also between the centroid and its associated points (intra-clustermerged, thus keeping track of the new cluster structure. The mean distance) is computed for each cluster and the globalmode merging step is repeated until no modes are merged. mean distance (mean of intra-cluster mean distances) is obtained for the whole partition. If this global mean distanceMembership of each point is finally determined by majority exceeds a threshold, the largest cluster in terms ofvoting. intra-cluster mean distance is split into two. The split is done by finding the main principal component ρ of the cluster and Graph clustering based on the normalized cut initializing two new child centroids at c ±d, where c is the centroid of the cluster to be split and d = ρ√2λ/π, with λ being The graph clustering algorithm based on the normalized the eigenvalue associated with the main principal componentcut proposed in [14] has become popular in the last years. ρ. After the split stage, the refinement stage consists ofHowever, the main drawback of this approach is that the applying k-means using the (C + 1) available centroids ascomputational technique for minimizing the normalized cut initial seeds. Both split and refinement are iterated until nois based on eigenvectors. Thus, it suffers from scalability new clusters are generated.problems, since in cases where the number of data points is The proposed wrapper has two main advantages over thevery large, eigenvector computation becomes prohibitive. classical k-means. First, instead of the desired number ofRecently, Dhillon et al. [15] proposed a more efficient clusters, the mean distance threshold controls the output oftechnique referred to as GRACLUS, which embeds a the algorithm.Such a threshold is more intuitive and closelyweighted kernel k-means algorithm into a multilevel related to perceptual properties than the number of clusters.approach in order to optimize locally the normalized cut. Second, the algorithm always behaves in the same way given However, before applying GRACLUS to the pattern the same input. Therefore, there is no need for runningdiscovery stage, the problem of specifying the number of different trials and keeping the best set of clusters accordingclusters must be addressed such as with k-means. Usually, to some criterion as it is the case when the initialization stepthe alternative is to first bipartition the whole graph and then of k-means has a random component.repartitions the already segmented parts if the normalized cutis below a specified value [14]. Colour-Based Segmentation Using K-Means Clustering Thebasicaimistosegmentcolorsinanautomatedfashionusingth II. K-MEANS CLUSTERING eL*a*b*colorspaceandK-meansThere are many methods of clustering developed for a wide clustering.Theentireprocesscanbesummarizedinfollowingstevariety of purposes. Clustering algorithms used for ps.unsupervised classification of remote sensing image data Step1:Readtheimagevary according to the efficiency with which clustering takes Readtheimagefrommother source (John R Jenson, 1986).K-means is the clustering Step2:ForcolorseparationofanimageapplytheDecoralgorithm used to determine the natural spectral groupings relationstretching.present in a data set. This accepts from analyst the number of Step3:ConvertImagefromRGBColorSpacetoL*a*b*ColorSpace. 565 All Rights Reserved © 2012 IJARCET
  4. 4. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Howmanycolorsdoweseeintheimage ifweignorevariations unsupervised problem into a supervised one.inbrightness? Therearethree colors:white,blue,andpink. As its name suggests, a pixel-based classifier aims atWecaneasilyvisuallydistinguish thesecolorsfromoneanother. determining the class to which every pixel of an input imageTheL*a*b*colorspace(alsoknownasCIELAB belongs, which leads to the segmentation of the image as aorCIEL*a*b*)enablesustoquantifythese visualdifferences. The collateral effect.L*a*b*colorspaceisderivedfromtheCIEXYZtristimulusvalues. In order to achieve this objective, several measures are computed for each image pixel by applying a number ofThe texture feature extraction methods as described in Section 3.1.L*a*b*spaceconsistsofaluminositylayerL*,chromaticity-layera*indicatingwherecolor falls alongthered-greenaxis,and Classification with multiple evaluation window sizeschromaticity-layerb*indicatingwherethecolorfallsalongthe Although previous works on supervised pixel-basedblue-yellow axis.Allofthecolorinformation classification have already shown the benefits of utilizingisinthea*andb*layers.Wecanmeasurethe difference multiple evaluation window sizes [10, 11], which approach isbetweentwocolorsusingtheEuclideandistancemetric.Convertthe the best for combining these different sources of information isimagetoL*a*b* colorspace. still an open issue.Step4:ClassifytheColorsina*b*SpaceUsingK-MeansClustering For instance, in [10], different window sizes were integrated. by assigning a weight to their corresponding probabilities Clusteringisa way according to how well each window size separates a giventoseparategroupsofobjects.K-meansclusteringtreatseach training pattern from the others. However, since the trainingobjectashaving alocationinspace. Itfindspartitions patterns are single-textured images, the assigned weight is notsuchthatobjectswithineachclusterareasclosetoeach representative of the structure of the test image, which in turn is composed of multiple texture patterns. Furthermore, thisotheraspossible,andas farfromobjectsinotherclustersas method may be biased to the largest window, as it capturespossible.K-meansclusteringrequires more information and, hence, has better capabilities ofthatyouspecifythenumberofclusters tobepartitioned distinguishing between texture classes. Later, in [11],andadistancemetrictoquantifyhow improved classification rates were obtained by directly fusingclosetwoobjectsaretoeachother.Sincethecolorinformation the outcome of multiple evaluation window sizes using theexistsinthea*b*space,your KNN rule. The main problem with this approach is that it doesobjectsarepixelswitha*andb*values. UseK-meanstocluster not guarantee that the most appropriate window size willtheobjectsintothreeclusters usingtheEuclideandistancemetric. always receive the majority of votes.Step5:LabelEveryPixelinthe Ideally, the strategy for classifying a test image usingImageUsingtheResultsfromK-MEANS multiple evaluation window sizes should apply large windows Foreveryobjectinourinput,K-meansreturnsanindexcorrespon inside regions of homogeneous texture in order to avoid noisy classified pixels and small windows near the boundariesding toacluster. Labelevery pixelin between those regions in order to define them precisely.theimagewithitsclusterindex. Unfortunately, that kind of knowledge about the structure ofStep6:CreateImagesthatSegmenttheImagebyColor. the image is only available after it has been segmented. Usingpixellabels,wehavetoseparateobjectsinimagebycolor, Notwithstanding, an a priori approximation of that strategy canwhichwillresultinfiveimages. be devised through the following steps:Step 7: Segment the Nuclei into a Separate Image Step 1: Select the largest available evaluation window and Then programmatically determine the index of the cluster classify the test image pixels labelled as unknown (initially, allcontaining the blue objects because K means will not return the pixels are labelled as unknown).same cluster idx value every time. We can do this using the Step 2: In the classified image, locate the pixels that belongcluster center value, which contains the mean a* and b* to boundaries between regions of different textures and markvalue for each cluster. them as unknown, as well as their neighbourhoods. The size of the neighbourhood corresponds to the size of the 1. Select k -seeds s.t. d ( ki , k j ) > d min window used to classify the image. Step 3: Discard the current evaluation window. 2. Assign points to clusters by min dist. Step4: Repeat steps 1 to 3 until the smallest evaluation Cluster ( pi ) = Arg min ( d ( pi , s j )) window has been utilized. This scheme, which can be thought of as a top-down s j { s1 ,…, sk } approach, has been used during the supervised classification 3. Compute new cluster centroids: stage of the proposed segmentation technique. In addition to  closely approximating the previously described ideal strategy  Cj  1  pi for using multiple evaluation window sizes, this approach avoids the classification of every image pixel with all the n pi  jthcluster available windows. Hence, it leads to a lower computation 4. Reassign points to clusters (as in 2 above) time than previous approaches. 5. Iterate until no points change clusters Supervised pixel-based classification III. RESULTS AND DISCUSSIONAt this stage, the set of texture patterns found by the previousstage are used as texture models for a supervised pixel based We implemented proposed algorithm and tested itsclassifier, thus effectively transforming the original performance on a number of standard images of Mat Lab 566 All Rights Reserved © 2012 IJARCET
  5. 5. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012software. We have used Peppers, Planet, Lena images fromMat Lab software as a standard image. Addition to theseimages we have implemented this proposed algorithm onheart image also & obtain segmentation results. Figure 1(a)shows original image of Peppers.png image and figure1(b)-1(g) show various segmented objects from originalimage. Here various color clusters and segmented objects areclearly visible. Table 1 shows parameter values ofPeppers.png image like Min, Max, mean, median, mode, Standard Deviation and range. Figure 1(h) showsthe scatter plot of original image Peppers.png. Figure 1 (i)shows Scatter plot with Bar and values of Peppers.png image.Figure 1 (j) shows Graph of Parameter values of Peppers.pngimage. Figure 1 (k) shows Radar Graph of Parameter valuesof Peppers.png image. Figure 2 (a) shows the second imageof our test data image of original Planets standard image frommat lab software. Figure 2(b) and 2(c) shows Object Figure 1 (c) Object Segmentation from Peppers image havingSegmentation from Planets image. Table 2 shows Parameter light green colorValues of Planets image. Figure 2(d) shows Scatter plot ofPlanets image. Figure 2 (e) represents Graph of Parametervalues of Planets image and Figure 2 (f) represents RadarGraph of Parameter values of Planets image. Similarly Figure3 shows results for Lena Image. Figure 4 shows segmentationresults of Heart image. Figure 1 (d) Object Segmentation from Peppers image having red colorFigure 1 (a) Original Peppers standard image from matlab Figure 1 (e) Object Segmentation from Peppers imageFigure 1 (b) Object Segmentation from Peppers image havingorange color 567 All Rights Reserved © 2012 IJARCET
  6. 6. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 220 60 200 50 180 Black 40 160 105 x min 150 x max 127 x mean 30 140 128 x median 136 x mode 120 9.173x std 20 Red Green 100 Violet 10 Magenta Yellow 80 100 120 140 160 180 200 220Figure 1 (f) Object Segmentation from Peppers image Figure 1 (i) Scatter plot with Bar and values of Peppers.png image Figure 1 (j) Graph of Parameter values of Peppers.png imageFigure 1 (g) Object Segmentation from Peppers image Black X Min Scatterplot of the segmented pixels in a*b* space Yellow Y250 Black Y 220 200 Max Yellow X 150 Red X 200 100 mean Magenta 50 0 Red Y median 180 Y Magenta mode Green X X b* values 160 std Violet Y Green Y 140 Violet X range 120 100 80 Figure 1 (k) Radar Graph of Parameter values of Peppers.png 100 120 140 160 180 200 220 a* values imageFigure 1 (h) Scatter plot of Peppers.png image 568 All Rights Reserved © 2012 IJARCET
  7. 7. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Table 1: Parameter Values of Peppers.png imagePeppers.png Min Max mean med mode STD ran geBlack X 105 150 127.21 128 136 9.1729 45Black Y 126 160 147.13 148 148 6.6843 34Red X 106 156 122.8 120 115 9.467 50Red Y 152 176 165 165 167 4.936 24Green X 156 201 183.05 185 187 7.9559 45Green Y 133 201 169.10 168 173 12.5043 68Violet X 128 179 155.5 156 168 12.93 51Violet Y 176 214 202.4 204 204 7.818 38Magenta X 110 156 126.3 123 121 9.347 37Magenta Y 163 200 181.3 182 185 6.347 37Yellow X 126 184 147.6 147 147 4.66 58Yellow Y 92 153 115.5 115 115 6.838 61 Figure 2(c) Object Segmentation from Planets image Scatterplot of the segmented pixels in a*b* space 200 180 160 b* values 140 120 100 80 60 120 130 140 150 160 170 180 190 200 Figure 2 (a) Original Planets standard image from matlab a* values Figure 2(d) Scatter plot of Planets image Table 2: Parameter Values of Planets image 250 Planets.jpg Min Max mean med mode std range Red X 120 161 134.2 133 132 4.6 41 200 Red Y 61 121 97.84 96 95 11.52 60 Violet X 120 199 134.7 131 128 12.01 79 150 Red X Violet Y 118 192 130.8 127 128 12.1 74 Red Y 100 Violet X 50 Violet Y 0 Figure 2 (e) Graph of Parameter values of Planets image Figure 2(b) Object Segmentation from Planets image 569 All Rights Reserved © 2012 IJARCET
  8. 8. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Min 200 range 150 Max 100 Red X 50 Red Y 0 std mean Violet X Violet Y mode median Figure 2 (f) Radar Graph of Parameter values of Planetsimage Figure 3(b) Object Segmentation from Lena image Figure 3(c) Object Segmentation from Lena image Figure 3 (a) Original standard image of Lena from matlab 570 All Rights Reserved © 2012 IJARCET
  9. 9. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Lena.tif Min Ma mea media mod std rang f x n n e e Black X 168 190 173.7 174 174 2.91 22 3 Black Y 140 187 151.3 151 149 3.99 47 1 Red X 166 182 171 171 172 2.40 16 2 Red Y 127 148 142 142 143 3.29 21 5 Green X 147 176 161 163 165 5.96 29 2 Green Y 124 143 133.6 134 141 5.32 19 2 Violet X 132 178 161.1 162 163 4.51 46 9 Violet Y 90 125 116.2 117 120 5.71 35 4 Magenta 125 148 139.5 139 138 3.63 23Figure 3(d) Object Segmentation from Lena image X Magenta 109 182 143.4 141 139 8.52 73 Y 3 Yellow 133 169 157.9 158 156 6.08 36 X 1 Yellow 142 210 152.1 151 146 6.85 68 Y 5 Table 3: Parameter Values of Lena image Scatterplot of the segmented pixels in a*b* space 220 200 180 b* values 160 140 120 100Figure 3(e) Object Segmentation from Lena image 80 120 130 140 150 160 170 180 190 a* values Figure 3(f) Scatter plot of Lena image 571 All Rights Reserved © 2012 IJARCET
  10. 10. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 250 200 Min 150 Max mean 100 median 50 mode std Figure 4 (b) Segmented object1 of Heart image 0 range Magenta X Magenta Y Violet X Violet Y Yellow Y Red Y Yellow X Red X Green Y Green X Black Y Black XFigure 3(g) Graph of Lena image parameter values Figure 4 (c) Segmented object2 of Heart image Min Black X Yellow 250 Y 200 Black Y Max Yellow X 150 Red X mean 100 Magenta 50 0 Red Y median Y Magenta mode Green X X Violet Y Green Y std Violet X range Figure 4 (d) Segmented object3 of Heart image Scatterplot of the segmented pixels in a*b* space 200 180Figure 3(h) Radar Graph plot of Lena image parametervalues 160 b* values 140 120 100 80 60 110 120 130 140 150 160 170 180 190 200 a* values Figure 4 (e) Scatter plot of Heart imageFigure 4 (a) Original image of Heart 572 All Rights Reserved © 2012 IJARCET
  11. 11. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012Figure 5 Quantitative Comparison of Segmentation Methods Table 4: Methods 573 All Rights Reserved © 2012 IJARCET
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Pattern Anal. 24 (2002) 603-619.Methods [22] J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. 22 (2000) 888-905. IV. CONCLUSION [23] I.S. Dhillon, Y. Guan, B. Kulis, Weighted graph cuts without eigenvectors: A multilevel approach, IEEE Trans. Pattern Anal. 29 (2007) 1944-1957.We have presented an efficient implementation of k-means [24] D. Tsujinishi, Y. Koshiba, S. Abe, Why pairwise is better thanclustering algorithm. The algorithm has been implemented oneagainst-all or all-at-once, in: Proceedings of the IEEE IJCNN,on standard images from mat lab software. Results are plotted 2004, scatter plots showing the clusters & Radar plot showing [25] W.-Y. Ma, B.S. Manjunath, Edge Flow: A technique for boundary detection and image segmentation, IEEE Trans. Image Process. 9the data analysis of clusters. Various segmentation methods (2000) 1375-1388.are given in form of chart. The plot of segmentation method [26] A.Y. Yang et al., Unsupervised segmentation of natural images viashows unsupervised k means cluster Method is better as lossydata compression, Comput. Vis. Image Und. 110 (2008) 212-225.compared to supervised classification segmentation methods. Janak B. Patel (born in 1971) received B.E.And the more well separated the clusters, the faster the (Electronics & Communication Engg from L.D.algorithm runs. This algorithm is significantly more efficient College of Engg. Ahmedabad, and M.E.than the other methods. (Electronics Communication & System Engg.) in 2000 from DDIT. He is Asst. Prof. & H.O.D. at REFERENCES L.D.R.P.I.T.R., Gujarat. He is pursuing Ph.D. at Indian Institute of Technology, Roorkee.[1] Ahmed Darwish, et al, Image Segmentation for the Purpose Of Object-Based Classification,, 2003 IEEE pp. 2039-2041 R R.S. Anand received B.E., M.E. and Ph.D. in[2] Darren MacDonald, et al; Evaluation of colour image segmentation Electrical Engg. from University of Roorkee in hierarchies, proceeding of the 3rd Canadian conference on 1985, 1987 and 1992, respectively. He is a Computer and Robot Vision, IEEE, 2006. professor at Indian Institute of Technology,[3] H C Chen et al, Visible color difference-based quantitative evaluation Roorkee. He has published more than 100 of colour segmentation, IEEE proceeding, Vis image signal process research papers in the area of image processing and vol.153 No.5 Oct 2006 pp 598-609. signal processing. 574 All Rights Reserved © 2012 IJARCET