期刊
PATTERN RECOGNITION
卷 41, 期 6, 页码 1924-1938出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2007.09.009
关键词
similarity based clustering; nonparametric density estimation; mean shift; connected components; spectral clustering
资金
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [0929576] Funding Source: National Science Foundation
In recent years there has been a growing interest in clustering methods stemming from the spectral decomposition of the data affinity matrix, which are shown to present good results on a wide variety of situations. However, a complete theoretical understanding of these methods in terms of data distributions is not yet well understood. In this paper, we propose a spectral clustering based mode merging method for mean shift as a theoretically well-founded approach that enables a probabilistic interpretation of affinity based clustering through kernel density estimation. This connection also allows principled kernel optimization and enables the use of anisotropic variable-size kernels to match local data structures. We demonstrate the proposed algorithm's performance on image segmentation applications and compare its clustering results with the well-known Mean Shift and Normalized Cut algorithms. (C) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据