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Mathematical Problems in Engineering
Volume 2015, Article ID 240354, 9 pages
http://dx.doi.org/10.1155/2015/240354
Research Article

A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

1School of Computer & Information Engineering, Anyang Normal University, Anyang 455002, China
2Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada E3B 5A3
3School of Software Engineering, Anyang Normal University, Anyang 455002, China

Received 24 October 2014; Accepted 1 January 2015

Academic Editor: Chih-Cheng Hung

Copyright © 2015 Guoying Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981. View at MathSciNet
  2. D. L. Pham, “Spatial models for fuzzy clustering,” Computer Vision and Image Understanding, vol. 84, no. 2, pp. 285–297, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 4, pp. 1907–1916, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Bilgin, S. Ertürk, and T. Yıldırım, “Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 4, pp. 673–677, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. L. He and I. R. Greenshields, “An MRF spatial fuzzy clustering method for fMRI SPMs,” Biomedical Signal Processing and Control, vol. 3, no. 4, pp. 327–333, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Xia, D. Feng, T. Wang, R. Zhao, and Y. Zhang, “Image segmentation by clustering of spatial patterns,” Pattern Recognition Letters, vol. 28, no. 12, pp. 1548–1555, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. S. P. Chatzis and T. A. Varvarigou, “A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 5, pp. 1351–1361, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Krinidis and V. Chatzis, “A robust fuzzy local information c-means clustering algorithm,” IEEE Transactions on Image Processing, vol. 19, no. 5, pp. 1328–1337, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. L. Lin, L. Zhu, F. Yang, and T. Jiang, “A novel pixon-representation for image segmentation based on Markov random field,” Image and Vision Computing, vol. 26, no. 11, pp. 1507–1514, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Yang and T. Jiang, “Pixon-based image segmentation with Markov random fields,” IEEE Transactions on Image Processing, vol. 12, no. 12, pp. 1552–1559, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. Q. Yu and D. A. Clausi, “IRGS: image segmentation using edge penalties and region growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2126–2139, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. A. K. Qin and D. A. Clausi, “Multivariate image segmentation using semantic region growing with adaptive edge penalty,” IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2157–2170, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. P. Yu, A. K. Qin, and D. A. Clausi, “Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 4, pp. 1302–1317, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. Prague, “The prague texture segmentation datagenerator and benchmark,” http://mosaic.utia.cas.cz/.
  16. J. Besag, “On the statistical analysis of dirty pictures,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 48, no. 3, pp. 259–302, 1986. View at Google Scholar · View at MathSciNet
  17. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721–741, 1984. View at Google Scholar · View at Scopus
  18. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Unnikrishnan and M. Hebert, “Measures of similarity,” in Proceedings of the 7th IEEE Workshop on Applications of Computer Vision (WACV '05), pp. 394–400, January 2005. View at Publisher · View at Google Scholar · View at Scopus