Robust Object Segmentation using Split and Merge - East West ...

(c)(d)Figure 8: (a) Original skeleton image, (b) Referenceimage of (a), (c)-(d) Segmentation results of the OSFand the proposed ROSSM algorithm respectively.Table 1: Statistics of qualitative performance of the OSFand the ROSSM algorithmsNomenclaturesOSFROSSMNo ofimagesNo ofimagesCorrectlySegmentedPartiallycorrectlysegmentedWronglysegmentedPercentageout of 211imagesPercentageout of 211images76 36.02% 152 72.04%62 29.38% 28 13.27%73 34.60% 31 14.69%To assess the robustness of the proposed ROSSMalgorithm, the experiments were conducted for 211different images having different objects containingdifferent shapes, sizes, orientations and features. Theexperimental results are shown in Table 1 where the OSFalgorithm produced correct segmentation results for 76images out of 211 images and hence the percentage ofsuccessfully segmenting images is 36.02% while theROSSM algorithm segments 152 images accurately with asuccess rate of 72.04%. The number of images for partiallycorrected and wrongly segmentation results for the OSF are62 and 73 respectively and for ROSSM algorithm for 28and 31 respectively. So, from this analysis, it can beconcluded that the ROSSM algorithm performs much betterin image segmentation than that of SM algorithm as well asthe OSF algorithm.6. ConclusionsThe Split and merge (SM) algorithm is well-knownalgorithm in digital image processing due to its simplicity,effectiveness, unsupervision and relative cost minimization,however, it is unable to achieve global optimum. Theproposed robust object segmentation based on split andmerge (ROSSM) considers region stability as the key issuefor splitting while the region stability, region variances, andhuman perception as key issues for multi stage merging. Inthe ROSSM algorithm, the thresholds used for merging arecalculated dynamically based on inter-and intra-variancesof regions. As a consequence, the ROSSM algorithm is ableto segment all type objects in an image. The experimentalresults have shown that the ROSSM algorithm hasoutperformed the SM algorithm and a shape-based fuzzyclustering algorithm namely object based imagesegmentation using fuzzy clustering (OSF). This increasesthe application area of the SM algorithm where thesegmentation is critical. The main problem of the proposedROSSM algorithm is that it is unable to segment similarand connected objects well due to considering pixelintensity while they may be identified as different objectsconsidering the spatial location and also if multipleattributes were considered the objects could be classifiedmore accurately. In the splitting stage a region is dividedinto four almost equal sub-regions if the threshold valuepermits. This hard partitioning splits the image region intonumerous unwanted sub-regions which need to rejoin(merge) in the merging stage that requires noticeable timefor merging. An efficient soft clustering which can dividethe base region into stable and an optimal numbers ofregions instead of simply dividing it into four regionswould be a further development.Reference[1] M. Ameer Ali, Laurence S Dooley and Gour CKarmakar, Object based Image Segmentation UsingFuzzy Clustering, ICASSP 2006.[2] K. Haris, et al., Hybrid Image Segmentation UsingWatersheds and Fast Region Merging, IEEETransactions on Image Processing, Vol. 7 (12),pp.1684–1699, 1998.[3] D. Chaudhuri, B.B. Chaudhuri and C.A. Murthy, ANew Split–and-Merge Clustering Technique, PatternRecognition Letters, Vol. 13, pp. 399-409, 1998.[4] T. Pavlidis, Structural Pattern Recognition, SpringerVerlag, New York: Berlin Heidelberg, 1977.[5] J.F. Canny, A computational approach to edgedetection, IEEE Transaction Pattern Analysis andMachine Intelligence, Vol. 8 (6), pp. 679–698, 1986.[6] R. Ronfard, Region-based strategies for activecontour models, International Journal of ComputerVision, Vol. 13(2), pp. 229–251, 1994.[7] G.M. Hunter, K.S. Steiglitz, Operations on ImagesUsing Quad-Tree, IEEE Transaction PatternAnalysis and Machine Intelligence, Vol. 1 (2), pp.145–153, 1979.[8] C. Revol, M. Jourlin, A new minimum varianceregion growing algorithm for image segmentation,Pattern Recognition Letters, Vol. 18, pp. 249–258,1997.[9] S.L. Horowitz, T. Pavlidis, Picture segmentation by adirected split-and-merge procedure, Proceedings ofthe 2nd International Joint Conference on PatternRecognition, pp. 424–433, 1974.[10] T. Brox, D. Firin and H. N. Peter, Multistage regionmerging for image segmentation, 22 nd Symposiumon Information theory in to Benelux, pp. 189-196,Enschede, 2001.