title: In-plane rotation and scale invariant clustering using dictionaries.
abstract: 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.
authors: Department of Electrical and Computer Engineering and the Center for Automation Research, UMIACS, University of Maryland, College Park, MD 20742, USA. cheny08@umiacs.umd.edu
authors: Yi-Chen
authors: YC
authors: Chen
authors: Challa S
authors: CS
authors: Sastry
authors: Vishal M
authors: VM
authors: Patel
authors: P Jonathon
authors: PJ
authors: Phillips
authors: Rama
authors: R
authors: Chellappa