International Journal of Fuzzy Logic and Intelligent Systems

  • 제10권1호
  • /
  • Pages.89-93
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  • 2010
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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VS-FCM: Validity-guided Spatial Fuzzy c-Means Clustering for Image Segmentation

  • Kang, Bo-Yeong (School of Mechanical Engineering, Kyungpook National University) ;
  • Kim, Dae-Won (School of Computer Science and Engineering, Chung-Ang University)
  • 투고 : 2009.09.14
  • 심사 : 2009.10.30
  • 발행 : 2010.03.25
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초록

In this paper a new fuzzy clustering approach to the color clustering problem has been proposed. To deal with the limitations of the traditional FCM algorithm, we propose a spatial homogeneity-based FCM algorithm. Moreover, the cluster validity index is employed to automatically determine the number of clusters for a given image. We refer to this method as VS-FCM algorithm. The effectiveness of the proposed method is demonstrated through various clustering examples.

키워드

참고문헌

  1. J.C. Bezdek et al, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer Academic Publishers, Boston, 1999.
  2. A. K. Jain, M. N. Murty, P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, vol. 31, pp. 264–323, 1999.
  3. N. S. Iyer, A. Kandel, M. Schneider, “Feature-based fuzzy classification for interpretation of mammograms,” Fuzzy Sets and Systems, vol. 114, pp. 271–280, 2000. https://doi.org/10.1016/S0165-0114(98)00175-4
  4. A. Shahin, M. Menard, M. Eboueya, “Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color doppler imaging,” Artificial Intelligence, vol. 19, pp. 121-154, 2000. https://doi.org/10.1016/S0933-3657(00)00042-7
  5. Y. A. Tolias, S. M. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,” IEEE Trans. Syst. Man Cybern. A, Syst. Humans, vol.28, pp.359-369, 1998. https://doi.org/10.1109/3468.668967
  6. A. W. C. Liew, S. H. Leung, W. H. Lau, “Fuzzy image clustering incorporating spatial continuity,” IEE Proceedings of Vis. Image Process, vol. 147, pp. 185-192, 2000. https://doi.org/10.1049/ip-vis:20000218
  7. D. Qzdemir, L. Akarun, “A fuzzy algorithm for color quantization of images,” Pattern Recognition, vol. 35, pp. 1785-1791, 2002. https://doi.org/10.1016/S0031-3203(01)00170-4
  8. D. W. Kim, K. H. Lee, D. Lee, “A novel initializa-tion scheme for the fuzzy c-means algorithm for color clustering,” Pattern Recognition Letters, vol. 25, pp. 227–237, 2004. https://doi.org/10.1016/j.patrec.2003.10.004
  9. G. Paschos, “Perceptually uniform color spaces for color texture and analysis,” IEEE Transactions on Image Processing, vol. 10, pp. 932–937,2001. https://doi.org/10.1109/83.923289
  10. H.-D. Cheng, Y. Sun, “A hierarchical approach to color image segmentation using homogeneity,” IEEE Transactions on Image Processing, vol. 9, pp. 2071-2082, 2000. https://doi.org/10.1109/83.887975
  11. D. W. Kim, K. H. Lee, D. Lee, “On cluster validity index for estimation of the optimal number of fuzzy clusters,” Pattern Recognition, vol. 37, pp. 2009-2025, 2004. https://doi.org/10.1016/j.patcog.2004.04.007
  12. J. T. Luke, The Munsell color system: a language for color, Fairchild Publications, New York, 1996.

피인용 문헌

  1. A Systematic Approach to Improve Fuzzy C-Mean Method based on Genetic Algorithm vol.13, pp.3, 2013, https://doi.org/10.5391/IJFIS.2013.13.3.178
  2. The power laws: Zipf and inverse Zipf for automated segmentation and classification of masses within mammograms vol.6, pp.3, 2015, https://doi.org/10.1007/s12530-014-9116-y