Hits:

Indexed by:会议论文

Date of Publication:2014-08-03

Included Journals:EI、CPCI-S、Scopus

Volume:8589

Page Number:230-240

Key Words:Fuzzy clustering; image segmentation; edge sensing; local information constraints; kernel function

Abstract: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.