Geometric Structure Based Image Clustering and Image Matching

摘要：

We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face images or object images taken with varying factors, the GGCI approach learns the global geometric structure of images space and clusters images based on geodesic distance instead of Euclidean distance and the extended nearest neighbor approach. The GSIM approach uses the minimal Euclidean distance between parts of image and the pattern and its variations as matching criteria and threshold strategy for image matching. We demonstrate experimentally that the GGCI approach achieves lower error rates and the GSIM approach brings down the sensitivity of gray values to change in radiometry and reduces multi local extrema to some extent.

关键词： geometric structure perception geodesic distance image clustering image matching.

作者: Sulan Zhang Chunqi Shi Zhiyong Zhang Zhongzhi Shi

作者单位: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 380-385

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）