Skip to main content
SpringerLink
Log in

Automatic Color Image Segmentation Using Spatial Constraint Based Clustering

  • Conference paper
  • First Online:
Emerging Trends in Computing and Communication

Part of the book series: Lecture Notes in Electrical Engineering ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294

    Article  Google Scholar 

  2. Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vis Graph Image Proc 29(1):100–132

    Article  Google Scholar 

  3. Cinque L, Foresti G, Lombardi L (2004) A clustering fuzzy approach for image segmentation. Pattern Recogn 37(9):1797–1807

    Article  MATH  Google Scholar 

  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 61–65

    Google Scholar 

  5. Xia Y, Wang T, Zhao R, Zhang Y (2007) Image segmentation by clustering of spatial patterns. Pattern Recogn Lett 28(12):1548–1555

    Article  Google Scholar 

  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 22–26

    Google Scholar 

  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 473–478

    Google Scholar 

  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):359–369

    Article  Google Scholar 

  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):825–838

    Article  MATH  Google Scholar 

  10. Dong G, Xie M (2005) Color clustering and learning for image segmentation based on neural networks. IEEE Trans Neural Networks 16(4):925–936

    Article  Google Scholar 

  11. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  12. Bezdek JC (1973) Cluster validity with fuzzy sets

    Google Scholar 

  13. Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1(1):57–71

    Article  MATH  MathSciNet  Google Scholar 

  14. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847

    Article  Google Scholar 

  15. Kwon SH (1998) Cluster validity index for fuzzy clustering. Electron Lett 34(22):2176–2177

    Article  Google Scholar 

  16. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11(7):773–780

    Article  Google Scholar 

  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 416–423

    Google Scholar 

  18. Jaccard P (1901) Etude comparative de la distribution floraledansune portion desAlpeset du Jura. Impr. Corbaz

    Google Scholar 

  19. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27

    Google Scholar 

  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 14

    Google Scholar 

  21. Thilagamani S (2011) A survey on image segmentation through clustering. Int J Res Rev Inf Sci (IJRRIS), 1(1):16–19

    Google Scholar 

  22. Seize TK (1977) Student’s t-test. South Med J 70(11):1299

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, West Bengal University of Technology, BF-142, Sector-I, Salt Lake, Kolkata, 700064, India

    Abu Shama & Santanu Phadikar

Authors
  1. Abu Shama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santanu Phadikar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santanu Phadikar .

Editor information

Editors and Affiliations

  1. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Sabnam Sengupta

  2. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Kunal Das

  3. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Gitosree Khan

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this paper

Cite this paper

Shama, A., Phadikar, S. (2014). Automatic Color Image Segmentation Using Spatial Constraint Based Clustering. In: Sengupta, S., Das, K., Khan, G. (eds) Emerging Trends in Computing and Communication. Lecture Notes in Electrical Engineering, vol 298. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1817-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1817-3_13

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1816-6

  • Online ISBN: 978-81-322-1817-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation