期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 34, 期 4, 页码 1907-1916出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2004.831165
关键词
fuzzy C-means clustering (FCM); image segmentation; kernel-induced distance measures; kernel methods; robustness; spatial constraints
Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L-2 norm). In this paper, to overcome the above problems, we first propose two variants, FCM_S-1 and FCM_S-2, of FCM_S to aim at simplifying its computation and then extend them, including FCM_S, to corresponding robust kernelized versions KFCM_S, KFCM_S-1 and KFCM_S-2 by the kernel methods. Our main motives of using the kernel methods consist in: inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data; enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective.
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