首页 | 本学科首页   官方微博 | 高级检索  
     


Image clustering based on sparse patch alignment framework
Authors:Jun Yu  Richang Hong  Meng Wang  Jane You
Affiliation:1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China;2. Department of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China;3. Department of Computing, The Hong Kong polytechnic University, Kowloon, Hong Kong
Abstract:Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points’ distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.
Keywords:Image clustering  Manifold learning  Sparse representation
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号