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论文类型：会议论文

发表时间：2014-08-03

收录刊物：EI、CPCI-S、Scopus

卷号：8589

页面范围：230-240

关键字：Fuzzy clustering; image segmentation; edge sensing; local information constraints; kernel function

摘要：In this paper, we present a variation of fuzzy local information c-means (FLICM) algorithm for image segmentation by introducing a novel tradeoff factor and an effective kernel metric. The proposed tradeoff factor utilizes both local spatial and gray level information in a new way, and the Euclidean distance in FLICM algorithm is substituted by Gaussian Radial Basis function. By the novel factor and kernel metric, the new algorithm has edge identification ability and is insensitive to noise. Experiments result on both synthetic and real world images show that the proposed algorithm is effective and efficient, providing higher segmenting accuracy than other competitive algorithms.