An Improved Fast Mean Shift Algorithm for Segmentation

摘要：

The mean shift algorithm is a statistical iterative algorithm based on kernel density estimation which has been widely used in many fields. This paper improves the mean shift algorithm by adopting the following approaches. Firstly, we present a novel approach named Random Sampling with Contexts (RSC) to speed up the mean shift algorithm. Secondly, we introduce Dempster-Shafer (D-S) theory for the fusion of features to improve the segmenting quality. Moreover, experimental results show that the new algorithm is superior to the typical mean shift algorithm.

关键词： mean shift kernel density estimation random sampling with contexts dempster-shafer theory

作者: Zhiming Qian Changren Zhu Runsheng Wang

作者单位: Room 4, Automatic Target Recognition Laboratory National University of Defense Technology Changsha, Hunan in China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 116-120

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）