17 December 1998 Finding regions of interest for content extraction
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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 non-parametric 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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric J. Pauwels, Eric J. Pauwels, Greet Frederix, Greet Frederix, } "Finding regions of interest for content extraction", Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333869; https://doi.org/10.1117/12.333869

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