An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications. (19th November 2006)

• Record Type: Journal Article
• Authors: Marques Marques, Oge Oge; Mayron Mayron, Liam M. Liam M.; Borba Borba, Gustavo B. Gustavo B.; Gamba Gamba, Humberto R. Humberto R.
• Other Names: Menegaz Menegaz Gloria Gloria Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-11-19
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/43450
• Languages: English
• ISSNs: 1687-6172

Abstract

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.