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Automatic Color Image Segmentation Using Spatial Constraint Based Clustering

  • Abu Shama
  • Santanu Phadikar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 298)

Abstract

Color image segmentation is a much talked about topic in image processing, where there is plenty of scope for improvement. A cluster validation index based novel method for automatic color image segmentation is proposed here. To identify the number of segments automatically cluster validity indices (Partition Coefficient, Partition Entropy, Xie-Beni index, Kwon’s index and Fuzzy hyper-volume index) have been used. Image has been segmented into the number of segments identified by cluster validation indices using modified Fuzzy C-means (FCM) algorithm, which not only uses the color values, but also the spatial relation of the pixels to identify the segment. The performance of the proposed segmentation algorithm has been evaluated using the benchmark data from Berkeley image segmentation dataset and also been compared with existing Otsu’s method, K-means algorithm and FCM algorithms based segmentation method using Jaccard Index (JI). Experimental results show that the proposed method gives better segmentation results both subjective and in terms of JI values.

Keywords

Color image segmentation Fuzzy C-means clustering Cluster validation index Automatic segmentation 

References

  1. 1.
    Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294CrossRefGoogle Scholar
  2. 2.
    Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vis Graph Image Proc 29(1):100–132CrossRefGoogle Scholar
  3. 3.
    Cinque L, Foresti G, Lombardi L (2004) A clustering fuzzy approach for image segmentation. Pattern Recogn 37(9):1797–1807CrossRefMATHGoogle Scholar
  4. 4.
    Ng HP, Ong SH, Foong KWC, Goh PS, Nowinski WL (2006) Medical image segmentation using K-means clustering and improved watershed algorithm. In: IEEE Southwest symposium on image analysis and interpretation, IEEE, Mar 2006, pp 61–65Google Scholar
  5. 5.
    Xia Y, Wang T, Zhao R, Zhang Y (2007) Image segmentation by clustering of spatial patterns. Pattern Recogn Lett 28(12):1548–1555CrossRefGoogle Scholar
  6. 6.
    Zhang XB, Jiang L (2009) An image segmentation algorithm based on fuzzy c-means clustering. In: International conference on digital image processing, Mar 2009, IEEE, pp 22–26Google Scholar
  7. 7.
    Shasidhar M, Raja VS, Kumar BV (2011) MRI brain image segmentation using modified fuzzy c-means clustering algorithm. In International conference on communication systems and network technologies (CSNT), June 2011, pp 473–478Google Scholar
  8. 8.
    Tolias YA, Panas SM (1998) Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans Syst Man Cybern Part A Syst Hum 28(3):359–369CrossRefGoogle Scholar
  9. 9.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40(3):825–838CrossRefMATHGoogle Scholar
  10. 10.
    Dong G, Xie M (2005) Color clustering and learning for image segmentation based on neural networks. IEEE Trans Neural Networks 16(4):925–936CrossRefGoogle Scholar
  11. 11.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, DordrechtCrossRefMATHGoogle Scholar
  12. 12.
    Bezdek JC (1973) Cluster validity with fuzzy setsGoogle Scholar
  13. 13.
    Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1(1):57–71CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847CrossRefGoogle Scholar
  15. 15.
    Kwon SH (1998) Cluster validity index for fuzzy clustering. Electron Lett 34(22):2176–2177CrossRefGoogle Scholar
  16. 16.
    Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11(7):773–780CrossRefGoogle Scholar
  17. 17.
    Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Eighth IEEE international conference on computer vision, ICCV 2001. Proceedings, IEEE, vol 2, pp 416–423Google Scholar
  18. 18.
    Jaccard P (1901) Etude comparative de la distribution floraledansune portion desAlpeset du Jura. Impr. CorbazGoogle Scholar
  19. 19.
    Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27Google Scholar
  20. 20.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, no 281–297, June 1967, p 14Google Scholar
  21. 21.
    Thilagamani S (2011) A survey on image segmentation through clustering. Int J Res Rev Inf Sci (IJRRIS), 1(1):16–19Google Scholar
  22. 22.
    Seize TK (1977) Student’s t-test. South Med J 70(11):1299CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of CSEWest Bengal University of TechnologyKolkataIndia

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