基于分级mean shift的图像分割算法  

Image Segmentation with Hierarchical Mean Shift

作　　者：汤杨[1,2] 潘志庚[1] 汤敏[3] 王平安[4] 夏德深[2] 

机构地区：[1]浙江大学CAD&CG国家重点实验室,杭州310027 [2]南京理工大学计算机科学与技术学院,南京210094 [3]河海大学计算机及信息工程学院,南京210098 [4]香港中文大学计算机科学与工程学系

出　　处：《计算机研究与发展》2009年第9期1424-1431,共8页Journal of Computer Research and Development

基　　金：国家自然科学基金重点项目(60533080);cast基金重点项目(cast2007041);国家自然科学基金项目(60773172)~~

摘　　要：实验发现传统mean shift算法进行分割时常会产生连接通道问题,使得几个分类簇之间无法完全分开.针对该问题,提出一种改进的分级mean shift图像分割算法,在初次迭代获得的聚类中心基础上采用不同的带宽矩阵进行多次聚类,从而获得不同级的聚类中心集合,并建立一个归属树结构,最终通过叶节点与根节点的归属关系进行归类从而完成图像分割.实验证明改进算法可以更好地保留图像的局部信息,同时具有较好的适用性.Connected channels were found somewhere in the feature space in traditional mean shift method. Consequently, to accomplish the segmentation by only one trial often leads to unsatisfactory result, especially in weak edges or regions without Gaussian character. To solve this problem, the authors design a hierarchical segment method based on mean shift technique. Upon on the original sampling data, clustering is performed iteratively with different bandwidths on the centers gained previously. Thus, a tree structure is established between the nodes in different levels. According to the inheritance relationship, the leaf nodes are merged and classified into different categories finally. The method was implemented and tested in both gray and color images. Compared with the traditional mean shift method, the hierarchical method has advantage to reserve the detaisl in the same scale. Also, although the multiple time clustering is needed, the computation cost will not increase obviously due to the decreasing sample sizes and varied bandwidths. The hierarchical mean shift method has been proved to be promising in the experiments. But more theoretical analysis is required and the method also needs to be improved by more experiments.

关 键 词：mean SHIFT 分级 图像分割 连接通道 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]