Finding Salient Regions in Images: Nonparametric Clustering for Image Segmentation and Grouping

• Published in: Computer vision and image understanding Vol. 75; no. 1-2; pp. 73 - 85
• Main Authors: Pauwels, E.J, Frederix, G
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-07-1999
• Subjects: connectivity; intermediate level processing; perceptual organization; segmentation; nearest neighbors; clustering; Turing test; content-based image retrieval; grouping
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1006/cviu.1999.0763

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.