Color Image Clustering Segmentation Based on Fuzzy Entropy and RPCL
基于模糊熵和RPCL的彩色图像聚类分割

This paper presents a clustering segmentation approach for color image based on fuzzy entropy and RPCL.It not only can adaptively detect the appropriate number and centers of the initial clusters of color image for RPCL and improve the learning rate,but also avoid over-segmentation caused by fuzzy entropy thresholding approach.Firstly fuzzy entropy of each color component is computed and initial clusters' centers of each color component are determined according to the fuzzy entropy curve.Then,these centers of different color components are combined to form the initial clusters' centers of color image.But the number of these combined clusters may be larger than that of the actual clusters,which may result in the over-segmentation.Therefore,RPCL is utilized to converge some of initial centers to actual centers of original color image and image is segmented by these learned cluster centers.The experiment shows that the method can effectively and adaptively segment color images without specifying the number and centers of initial clusters in advance.