聚类分析在彩色图像量化中的应用

Applications of Clustering Analysis in Color Image Quantization

提出了一种基于模式识别技术的彩色图像量化的新算法—基于最小距离最大的快速统计聚类算法(FSCAMMD)。本算法克服了SCA算法对聚类中心初始值选取的不足,给出了最大频度与类内最小距离最大相结合的方法—初始值优选法。实验结果表明,本算法可较大幅度地减少图像量化后的总方差以及颜色失真度,量化效果优于SCA和其它一些聚类量化算法。

A new algorithm for color image quantization based on the pattern recognition technology is proposed in this paper. This is a fast statistical clustering algorithm based on maximizing minimum discrepancy (FSCAMMD). The algorithm can overcome the shortcomings of the seeking method of initial value of the clustering center of SCA algorithm, and gives a method of combining maximum frequency degree with maximizing minimum discrepancy, that is an optimum seeking method of initial value of clustering center. The experimental results show that 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.