Color Image Adaptive Clustering Segmentation

Authors:
Guizhi Li;Chengwan An;Jie Pang;Min Tan;Xuyan Tu
Affiliations:
Beijing Institute of Machinery and University of Science and Technology Beijing;Chinese Academy of Sciences;University of Science and Technology Beijing;Chinese Academy of Sciences;University of Science and Technology Beijing
Venue:
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Year:
2004

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Abstract

This paper presents an adaptive clustering segmentation approach based on fuzzy entropy and Rival Penalized Competitive Learning (RPCL) for color image. It can adaptively acquire appropriate number of color clusters and their centers of color image. Firstly, fuzzy entropy approach is applied to smooth color componentsý histograms and centers of each color component are determined. Then these centers of different color components are combined to form initial centers for RPCL. Finally, RPCL converges some of initial centers to actual centers of original color image and pushes other initial centers away and image is segmented by the former learned centers. The experiment shows that the method can effectively and adaptively segment color images without specifying the number of initial clusters in advance.