An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications
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Research Article An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications Oge Marques,1 Liam M. Mayron,1 Gustavo B. Borba,2 and Humberto R. Gamba2 1 Department
of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431-0991, USA ˜ Curitiba, Paran´a 80230-901, Brazil
2 Programa de P´ os-Graduac¸ao em Engenharia El´etrica e Inform´atica Industrial, Universidade Tecnol´ogica Federal do Paran´a (UTFPR),
Received 1 December 2005; Revised 3 August 2006; Accepted 26 August 2006 Recommended by Gloria Menegaz 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. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
The dramatic growth in the amount of digital images available for consumption and the popularity of inexpensive hardware and software for acquiring, storing, and distributing images have fostered considerable research activity in the field of content-based image retrieval (CBIR) [1] during the past decade [2, 3]. Simply put, in a CBIR system users search the image repository providing information about the actual contents of the image, which is often done using another image as an example. A content-based search engine translates this information in some way as to query the database (based on previously extracted and stored indexes) and retrieve the candidates that are more likely to satisfy the user’s request. In spite of the large number of related papers, prototypes, and several commercial solutions, the CBIR problem has not been satisfactorily solved. Some of the open problems include the gap between the image features that can be extracted using image processing algorithms and the semantic concepts to which they may be related (the well-known semantic gap problem [4–6], which can often be translated as “the discrepancy between the query a user id
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