Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

Authors:
Feng Zhao;Licheng Jiao;Hanqiang Liu
Affiliations:
School of Communication and Information Engineering, Xian University of Posts and Telecommunications, Xian, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xian, PR China;School of Computer Science, Shaanxi Normal University, Xian, PR China
Venue:
Digital Signal Processing
Year:
2013

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Abstract

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray images. In order to overcome GFCM@?s sensitivity to noise in the image, a kernel version of GFCM with spatial information is proposed. In this method, first a term about the spatial constraints derived from the image is introduced into the objective function of GFCM, and then the kernel induced distance is adopted to substitute the Euclidean distance in the new objective function. Experimental results show that the proposed method behaves well in segmentation performance and convergence speed for gray images corrupted by noise.