Human Object Segmentation Using Gaussian Mixture Model and Graph Cuts

摘要：

In this paper, we propose an efficient approach to automatic human object segmentation. First, foreground (human object) model and background model are built based on the face detection result, and are used to obtain the seed pixels for foreground and background, respectively. Then seed pixels are clustered using K-means algorithm, and Gaussian mixture models are exploited to generate the foreground/background probability map. Finally, pixels are efficiently classified into foreground and background under the framework of graph cuts. Experimental results on a variety of video sequences demonstrate the better segmentation performance of the proposed approach.

作者: Baoyan Ding Ran Shi Zhi Liu Zhaoyang Zhang

作者单位: School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China

会议类型: 国际会议

会议名称: 第十届中国虚拟现实年会

会议地点: 上海

会议语种:英文

页码: 787-790

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）