Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation

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• 作者：Yifang Yang (17)
  Yuping Wang (17)
  Yiu-ming Cheung (18)
  
• 关键词：spectral clustering ; kernel fuzzy ; clustering ; image segmentation ; Nystr枚m method
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8008
• 期：1
• 页码：254-261
• 全文大小：308KB
• 参考文献：1. Shi, J., Malik, J.: Normalized cuts and image segmentation. In: IEEE Conf. Computer Vision and Pattern Recognition, vol.聽3(2), pp. 731鈥?37. IEEE Computer Society (1997)
  2. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys聽38(4), 1鈥?5 (2006) CrossRef
  3. Chen, S.C., Zhang, D.Q.: 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聽34(4), 1907鈥?916 (2004) CrossRef
  4. Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nystr枚m method. IEEE Transactions on Pattern Analysis and Machine Intelligence聽26(2), 214鈥?25 (2004) CrossRef
  5. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence聽22(8), 888鈥?05 (2000) CrossRef
  6. Zhao, F., Jiao, L.C., Liu, H., Gao, X.B.: A novel fuzzy clustering algorithm with nonlocal adaptive spatial constraint for image segmentation. Signal Processing聽91(4), 988鈥?99 (2011) CrossRef
  7. Liu, H., Zhao, F., Jiao, L.: Fuzzy spectral clustering with robust spatial information for image segmentation. Applied Soft Computing聽12, 3636鈥?647 (2012) CrossRef
  8. Liu, H.Q., Jiao, L.C., Zhao, F.: Non-local spatial spectral clustering for image segmentation. Neurocomputing聽74(1-3), 461鈥?71 (2011) CrossRef
  9. Gou, S.P., Zhuang, X., Jiao, L.C.: Quantum Immune Fast Spectral Clustering for SAR Image Segmentation. IEEE Geoscience and Remote Sensing Letters聽9(1) (January 2012)
  10. Chen, W., Feng, G.: Spectral clustering with discriminant cuts. Knowledge-Based Systems聽28, 27鈥?7 (2012) CrossRef
  11. Rebagliati, N., Verri, A.: Spectral clustering with more than K eigenvectors. Neurocomputing聽74, 1391鈥?401 (2011) CrossRef
  12. Su, M.C., Chou, C.H.: A modied version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell.聽23, 674鈥?80 (2001) CrossRef
  13. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Eighteenth Neural Information Processing Systems (NIPS), Vancouver, Canada, pp. 1601鈥?608 (2004)
  14. Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recognit.聽38(4), 607鈥?11 (2005) CrossRef
  15. Graves, D., Pedrycz, W.: Performance of kernel-based fuzzy clustering. Electron. Lett.聽43(25), 1445鈥?446 (2007) CrossRef
  16. Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets Syst.聽161(4), 522鈥?43 (2010) CrossRef
  17. Chen, L., Chen, C.L.P., Lu, M.: A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics聽41(5), 1263鈥?274 (2011) CrossRef
  18. Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med.聽32(1), 37鈥?0 (2004) CrossRef
  19. Tsai, D.-M., Lin, C.-C.: Fuzzy C-means based clustering for linearly and nonlinearly separable data. Pattern Recognition聽44, 1750鈥?760 (2011) CrossRef
  20. Zhang, X., Li, J., Yu, H.: Local density adaptive similarity measurement for spectral clustering. Pattern Recognition Letters聽32, 352鈥?58 (2011) CrossRef
  21. Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans. Pattern Anal. Machine Intell.聽25(4), 513鈥?18 (2003) CrossRef
  22. Chang, H., Yeung, D.-Y.: Robust path-based spectral clustering. Pattern Recognit.聽41(1), 191鈥?03 (2008) CrossRef
  23. Zhao, F., Liu, H., Jiao, L.: Spectral clustering with fuzzy similarity measure. Digital Signal Processing聽21, 701鈥?09 (2011) CrossRef
  24. Zeyu, L., Shiwei, T., Jing, X., Jun, J.: Modified FCM clustering based on kernel mapping. In: Proc. of the Internat. Society for Optical Engineering, vol.聽4554, pp. 241鈥?45 (2001)
  25. Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets and Systems聽161, 522鈥?43 (2010) CrossRef
  26. Bach, F., Jordan, M.: Learning spectral clustering. In: Proceedings of NIPS 2003, pp. 305鈥?12 (2003)
  
• 作者单位：Yifang Yang (17)
  Yuping Wang (17)
  Yiu-ming Cheung (18)
  
  17. School of Computer Science and Technology, Xidian University, Xi鈥檃n, 710071, China
  18. Department of Computer Science, Hong Kong Baptist University, Hong Kong
  

文摘

Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr $\ddot{o}$ m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.