摘要
针对传统HCM算法运算时间过长且易陷入局部最优解的缺点,提出一种结合金字塔结构与减法聚类的HCM算法.该算法先将图像描述为不同尺度上的金字塔图像序列,对顶层图像运用减法聚类确定初始中心后进行HCM,然后依次将上一层图像的聚类结果作为初始中心对本层图像进行HCM聚类,对最底层聚类得到的结果即是最终的聚类结果.仿真试验表明,该算法的运行时间远远低于传统HCM算法,且聚类质量比传统HCM算法好.
Aiming at two disadvantages of the traditional HCM, which are its too long running time and its easy getting into local optimum solution, the HCM algorithm combined with pyramidal structure and subtractive clustering is put forward. This algorithm uses the pyramidal structure to describe the image in different scales, and carries out HCM after getting the initial center by the subtractive clustering in the top image. Then HCM is done in the image at this layer with cluster result of upper image as the initial center, the result of the bottom image is the final clustering result. Simulation experiments show that the running time of this algorithm is far shorter than that of traditional HCM, and clustering quality also is better.
出处
《光电技术应用》
2009年第1期73-77,共5页
Electro-Optic Technology Application
关键词
硬C-均值聚类
减法聚类
颜色聚类
金字塔结构
hard c-means（HCM）
subtractive clustering
color clustering
pyramidal structure
