EURASIP Journal on Advances in Signal Processing
Volume 2007 (2007), Article ID 43450, 17 pages
doi:10.1155/2007/43450
Research Article
An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications
1Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton 33431-0991, FL, USA
2Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná
(UTFPR), Curitiba 80230-901, Paraná, Brazil
Received 1 December 2005; Revised 3 August 2006; Accepted 26 August 2006
Academic Editor: Gloria Menegaz
Copyright © 2007 Oge Marques et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement. This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem. We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly. Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism.