¹College of Computer Science, Sichuan University, Chengdu 610064, China
²State Key Laboratory of Fundamental Science on Synthetic Vision, Chengdu 610064, China
³College of Computer Science, Panzhihua University, Sichuan Panzhihua 610007, China

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Abstract—FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results show that KSFCM has the better performance.

Index Terms—Kernel, spatial information, segmentation, FCM.

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Cite: Cuiyin Lui, Xiuqiong Zhang, Xiaofeng Li, Yani Liu, Jun Yang, "Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation," Journal of Computers vol. 7, no. 6, pp. 1511-1518, 2012.