Finding salient regions in images - Nonparametric clustering for image segmentation and grouping

被引:83
作者
Pauwels, EJ [1 ]
Frederix, G [1 ]
机构
[1] Katholieke Univ Leuven, Dept Elect Engn, PSI, ESAT, B-3001 Louvain, Belgium
关键词
clustering; segmentation; grouping; perceptual organization; intermediate level processing; content-based image retrieval; connectivity; nearest neighbors; Turing test;
D O I
10.1006/cviu.1999.0763
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A major problem in content-based image retrieval (CBIR) is the unsupervised identification of perceptually salient regions in images. We contend that this problem can be tackled by mapping the pixels into various feature-spaces, whereupon they are subjected to a grouping algorithm. In this paper we develop a robust and versatile nonparametric clustering algorithm that is able to handle the unbalanced and highly irregular clusters encountered in such CBIR applications. The strength of our approach lies not so much in the clustering itself, but rather in the definition and use of two cluster-validity indices that are independent of the cluster topology. By combining them, an optimal clustering can be identified, and experiments confirm that the associated clusters do, indeed, correspond to perceptually salient image regions. (C) 1999 Academic Press.
引用
收藏
页码:73 / 85
页数:13
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