EFFICIENT IMAGE CLUSTERING USING A NEW IMAGE DISTANCE

摘要：

A new distance for image clustering called Generalized Geodesic Distance (GGD) and an appearance-based image clustering approach called Global Geometric Clustering for Image (GGCI) are presented.Unlike the traditional distance, GGD takes into account the spatial relationships of images.Therefore, it is robust to small perturbation of images.GGCI based on GGD uses easily measured local metric information to learn the underlying global geometry of images space, then applies the extended nearest neighbor approach to cluster images.Different from the usual nearest neighbor approach, GGCI considers the density around the nearest points within manifolds embedded in high dimensional image space, which better reflects the intrinsic geometric structure of manifold.Experimental results suggest that the proposed GGCI approach achieves lower error rates in image clustering.

关键词： Geodesic distance Manifold Image clustering

作者: SU-LAN ZHANG QING HE ZHONG-ZHI 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: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1601-1605

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）