IEEE Trans Image Process - In-plane rotation and scale invariant clustering using dictionaries.

Tópicos

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Resumo

In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.

Resumo Limpo

paper present approach simultan cluster imag learn dictionari cluster method learn dictionari cluster imag radon transform domain main featur propos approach provid inplan rotat scale invari cluster use numer applic includ contentbas imag retriev cbir demonstr effect rotat scale invari cluster method seri cbir experi experi perform smithsonian isol leaf kimia shape brodatz textur dataset method provid good retriev perform greater robust compar standard gaborbas three stateoftheart shapebas method similar object

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