聚类分析在彩色图像量化中的应用

聚类分析是数据挖掘领域中的一个重要研究方向。它在统计数据分析、模式识别、图像处理等领域有着广泛的应用。迄今为止,人们已经提出了许多用于彩色图像量化的聚类算法。在以往的算法中,由于普遍存在着对初始聚类中心选取的盲目性或极端性,故使重建图像的整体层次与局部细节之间的矛盾未得到较好的解决。该文以SCA犤6犦算法为基础,给出了一种基于最大频度与类内最小距离最大相结合的初始聚类中心优选法———SCAMMD聚类算法。实验表明:该算法能较大幅度地减少图像量化后的均方差以及颜色失真度,量化效果比SCA和其它一些聚类量化算法有显著的提高。

Applications of Clustering Analysis in Color Image Quantization

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.