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Bio-Inspired Scheme for Classification of Visual Information

Bio-Inspired Scheme for Classification of Visual Information

Le Dong, Ebroul Izquierdo, Shuzhi Ge
Copyright: © 2011 |Pages: 25
ISBN13: 9781609600242|ISBN10: 160960024X|EISBN13: 9781609600266
DOI: 10.4018/978-1-60960-024-2.ch014
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MLA

Dong, Le, et al. "Bio-Inspired Scheme for Classification of Visual Information." Computer Vision for Multimedia Applications: Methods and Solutions, edited by Jinjun Wang, et al., IGI Global, 2011, pp. 238-262. https://doi.org/10.4018/978-1-60960-024-2.ch014

APA

Dong, L., Izquierdo, E., & Ge, S. (2011). Bio-Inspired Scheme for Classification of Visual Information. In J. Wang, J. Cheng, & S. Jiang (Eds.), Computer Vision for Multimedia Applications: Methods and Solutions (pp. 238-262). IGI Global. https://doi.org/10.4018/978-1-60960-024-2.ch014

Chicago

Dong, Le, Ebroul Izquierdo, and Shuzhi Ge. "Bio-Inspired Scheme for Classification of Visual Information." In Computer Vision for Multimedia Applications: Methods and Solutions, edited by Jinjun Wang, Jian Cheng, and Shuqiang Jiang, 238-262. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-024-2.ch014

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Abstract

In this chapter, research on visual information classification based on biologically inspired visually selective attention with knowledge structuring is presented. The research objective is to develop visual models and corresponding algorithms to automatically extract features from selective essential areas of natural images, and finally, to achieve knowledge structuring and classification within a structural description scheme. The proposed scheme consists of three main aspects: biologically inspired visually selective attention, knowledge structuring and classification of visual information. Biologically inspired visually selective attention closely follow the mechanisms of the visual “what” and “where” pathways in the human brain. The proposed visually selective attention model uses a bottom-up approach to generate essential areas based on low-level features extracted from natural images. This model also exploits a low-level top-down selective attention mechanism which performs decisions on interesting objects by human interaction with preference or refusal inclination. Knowledge structuring automatically creates a relevance map from essential areas generated by visually selective attention. The developed algorithms derive a set of well-structured representations from low-level description to drive the final classification. The knowledge structuring relays on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone is a distribution mapping strategy involving two novel modules: structured low-level feature extraction using convolution neural network and topology preservation based on sparse representation and unsupervised learning algorithm. Classification is achieved by simulating high-level top-down visual information perception and classification using an incremental Bayesian parameter estimation method. The utility of the proposed scheme for solving relevant research problems is validated. The proposed modular architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.

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