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Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm

by Amiya Halder, Soumajit Pramanik, Arindam Kar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 6
Year of Publication: 2011
Authors: Amiya Halder, Soumajit Pramanik, Arindam Kar
10.5120/3392-4714

Amiya Halder, Soumajit Pramanik, Arindam Kar . Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm. International Journal of Computer Applications. 28, 6 ( August 2011), 15-20. DOI=10.5120/3392-4714

@article{ 10.5120/3392-4714,
author = { Amiya Halder, Soumajit Pramanik, Arindam Kar },
title = { Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 6 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number6/3392-4714/ },
doi = { 10.5120/3392-4714 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:02.956017+05:30
%A Amiya Halder
%A Soumajit Pramanik
%A Arindam Kar
%T Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 6
%P 15-20
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes an evolutionary approach for unsupervised gray-scale image segmentation that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, fuzzy c-means clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.

References
  1. Rafael. C. Gonzalez, Richard. E. Woods, Digital Image Processing, Pearson Education, 2002.
  2. S. Z. Selim, M. A. Ismail, K-means Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality, IEEE Trans. Pattern Anal. Mach. Intell. 6, (1984), 81-87.
  3. E.Forgy, Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification, Biometrics,21: 1965.
  4. J. A. Hartigan, Clustering Algorithms, John Wiley Sons, New York, 1975.
  5. G. Ball, D. Hall, A Clustering Technique for Summarizing Multivariate Data, Behavioral Science, 12: 1967.
  6. T. Kohonen, Self-Organizing Maps, Springer Series in Information Sciences, Vol 30, Springer-Verlag, 1995.
  7. DW. Vander. Merwe, AP Engelbrecht, Data Clustering using Particle Swarm Optimization.
  8. LV Fausett, Fundamentals of Neural Networks, Prentice Hall, 1994.
  9. Ujjwal Maulik and Sangamitra Bandyopadhyay, Genetic Algorithm based clustering technique, Elsevier Sceince Ltd., 1999.
  10. Hwe Jen Lin, Fu-Wen Yang and Yang-Ta Kao, An Efficient GA-based Clustering Technique, Tamkang Journal of Science and Engineering 8(2), 2005.
  11. R.H.Turi, Clustering-Based Color Image Segmentation, PhD Thesis, Monash University, Australia, 2001.
  12. Mofakharul Islam, John Yearwood and Peter Vamplew, Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model, Advances in Computer and Information Sciences and Engineering, 323-328, 2008.
  13. Dipak Kumar Kole and Amiya Halder, An efficient dynamic Image Segmentation algorithm using Dynamik GA based clustering, International Journal of Logistics and Supply Chain Management, 2(1), pp. 17-20, 2010.
  14. Amiya Halder and Nilavra Pathak, An Evolutionary Dynamic Clustering Based Colour Image Segmentation, International Journal of Image Processing (IJIP), Volume (4): Issue (6), pp. 549-556, 2011.
  15. Keh-Shih Chuang , Hong-Long Tzeng , Sharon Chen, Jay Wu, Tzong-Jer Chen, Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics Vol. 30, pp. 9–15 ,2006.
  16. Jzau-Sheng Lin, Kuo-Sheng Cheng,Chi-Wu Mao, A Fuzzy Hopfield Neural Network for Medical Image Segmentation, IEEE Transactions on Nuclear Science, Vol. 43, No. 4, pp.2389-2398, August 1996.
  17. Mahamed G. H. Omran, Andries P Engelbrecht and Ayed Salman, Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification, PWASET 9:2005.
  18. Arun K Pujari, Data Mining Techniques, Universities Press, 2003.
  19. Indrajit Saha, Ujjwal Maulik and Sanghamitra Bandyopadhyay, An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation, EvoWorkshops 2009, LNCS 5484, pp. 426–431, 2009.
  20. M. Srinivas, Lalit M. Patnaik, Genetic Algorithms: A Survey.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Image Segmentation Fuzzy C-means Genetic Algorithm

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