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Publication Citation: In-plane Rotation and Scale Invariant Clustering and Dictionary Learning

NIST Authors in Bold

Author(s): P J. Phillips; Challa Sastry; Yi-Chen Chen; Vishal M. Patel; Rama Chellappa;
Title: In-plane Rotation and Scale Invariant Clustering and Dictionary Learning
Published: June 03, 2013
Abstract: n this paper, we present an approach that simulta- neously clusters images and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image 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. The 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.
Citation: IEEE Transactions on Image Processing
Volume: 22
Issue: 6
Pages: pp. 2166 - 2180
Research Areas: Imaging
PDF version: PDF Document Click here to retrieve PDF version of paper (6MB)