A Fast Clustering Algorithm for Color Images Quantization
一种用于彩色图象量化的快速聚类算法

A new algorithm for color image quantization based on the pattern recognition technology is proposed in this paper. First, the color samples in a color image are grouped together, and the initial representative points of the categories are chosen based upon a method of combining maximum frequency degree with maximizing minimum discrepancy, that is , an optimum seeking method of initial value of clustering center. Then both the clustering criteria of Euclidean distance in clustering analysis and the gravitational center method in mechanics are used to determine the vector values of the new clustering region centers, and the satisfying clustering effects can de obtained. This is a fast statistical clustering algorithm based on maximizing minimum discrepancy (FSCAMMD). The presented algorithm can overcome the shortcomings of the seeking method of initial value of the clustering center of SCA algorithm. Both the total mean square deviation and lack fidelity of images quantized by the present algorithm have a relatively big reduction and the effect of color image equalization is better than that of SCA algorithm and other clustering algorithms.