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Reseach Article

Color Image Segmentation using ERKFCM

by C. Mythili, V.kavitha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 20
Year of Publication: 2012
Authors: C. Mythili, V.kavitha
10.5120/5809-8074

C. Mythili, V.kavitha . Color Image Segmentation using ERKFCM. International Journal of Computer Applications. 41, 20 ( March 2012), 21-28. DOI=10.5120/5809-8074

@article{ 10.5120/5809-8074,
author = { C. Mythili, V.kavitha },
title = { Color Image Segmentation using ERKFCM },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 20 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number20/5809-8074/ },
doi = { 10.5120/5809-8074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:06.798946+05:30
%A C. Mythili
%A V.kavitha
%T Color Image Segmentation using ERKFCM
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 20
%P 21-28
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color image segmentation is an important task for computer vision. The segmented RGB color space is not more reliable and accurate for computer vision applications. For this purpose, the proposed approach combines different color spaces such as RGB, HSV, YIQ and XYZ for image segmentation. The combine segmentation of various color spaces to give more accurate segmentation result compared to segmentation of single color space. The images are segmented using K- means clustering and Effective robust kernelized fuzzy c-means(ERKFCM). Two significant criteria namely PSNR (Peak Signal to Noise Ratio) and MSE (Mean square error) are used to evaluate the performance.

References
  1. M. Borsotti, P. Campadelli, and R. Schettini, Quantitative evaluation of color image segmentation results, Pattern Recognition letters, vol. 19, no. 8, pp. 741-48, 1998.
  2. Y. Delignon, et. al. , Estimation of generalized mixtures and its application in image segmentation, IEEE Trans. on Image Processing, vol. 6, no. 10, p. 1364-76, 1997.
  3. W. Y. Ma and B. S. Manjunath, Edge flow: a framework of boundary detection and image segmentation, Proc. of CVPR, pp 744-49, 1997.
  4. D. K. Panjwani and G. Healey, Markov random field models for unsupervised segmentation of textured images, PAMI, vol. 17, no. 10, p. 939-54, 1995.
  5. L. Shafarenko, M. Petrou, and J. Kittler,Automatic watershed segmentation of randomly textured color images, IEEE Trans. on Image Processing, vol. 6, no. 11, p. 1530-44, 1997.
  6. J. Shi and J. Malik, Normalized cuts and image segmentation, Proc. of CVPR, p. 731-37, 1997.
  7. H. Stokman and T. Gevers, ?Selection and fusion of color models for image feature detection, IEEE Trans. Pattern Anal. Mach. Intell. , vol. 29, no. 3, pp. 371–381, Mar. 2007.
  8. J. -P. Wang, Stochastic relaxation on partitions with connected components and its application to image segmentation, PAMI, vol. 20, no. 6, p. 619-36, 1998.
  9. A. Y. Yang, J. Wright, S. Sastry, and Y. Ma,?Unsupervised segmentation of natural images via lossy data compression, Comput. Vis. Image Understand. , 2007, submitted for publication.
  10. S. C. Zhu and A. Yuille, Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, PAMI, vol. 18, no. 9, p. 884-900.
  11. Max mignotte, A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation, IEEE transaction on image processing, Vol 19, No. 6, June 2010.
  12. Nuno Vieira lopes, pedro A. Mogadouro, Automatic histogram threshold using fuzzy measures, IEEE transaction on image processing, Vol. 19, No. 1, pp 199-204.
  13. G. Uma maheswari, K. Ramar, D. Manimegalai, V. Gomathi, An adaptive region based color texture segmentation using fuzzified distance, 2011, pp 2916-2924.
  14. Xiang-Yang wang, ting wang and juan bu, Color image segmentation using pixel wise support vector machine classification, Patten recognition, 2011, pp 777-781.
  15. Keh-shih chuang, Hong-long Tzeng, Sharon chaen, Jay wu, Tzong-Jer chen, Fuzzy C-means clustering with spatial information for image segmentation, Computerized medical imaging and graphics, 2006, pp 9-15.
Index Terms

Computer Science
Information Sciences

Keywords

Color Image Segmentation Color Spaces K-means Clustering Erkfcm And Image Fusion