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A Novel Plausible Model for Visual Perception

A Novel Plausible Model for Visual Perception

Zhiwei Shi, Zhongzhi Shi, Hong Hu
Copyright: © 2010 |Pages: 17
ISBN13: 9781605669021|ISBN10: 1605669024|ISBN13 Softcover: 9781616924140|EISBN13: 9781605669038
DOI: 10.4018/978-1-60566-902-1.ch023
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MLA

Shi, Zhiwei, et al. "A Novel Plausible Model for Visual Perception." Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, IGI Global, 2010, pp. 428-444. https://doi.org/10.4018/978-1-60566-902-1.ch023

APA

Shi, Z., Shi, Z., & Hu, H. (2010). A Novel Plausible Model for Visual Perception. In Y. Wang (Ed.), Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (pp. 428-444). IGI Global. https://doi.org/10.4018/978-1-60566-902-1.ch023

Chicago

Shi, Zhiwei, Zhongzhi Shi, and Hong Hu. "A Novel Plausible Model for Visual Perception." In Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, 428-444. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-902-1.ch023

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Abstract

Traditionally, how to bridge the gap between low-level visual features and high-level semantic concepts has been a tough task for researchers. In this article, we propose a novel plausible model, namely cellular Bayesian networks (CBNs), to model the process of visual perception. The new model takes advantage of both the low-level visual features, such as colors, textures, and shapes, of target objects and the interrelationship between the known objects, and integrates them into a Bayesian framework, which possesses both firm theoretical foundation and wide practical applications. The novel model successfully overcomes some weakness of traditional Bayesian Network (BN), which prohibits BN being applied to large-scale cognitive problem. The experimental simulation also demonstrates that the CBNs model outperforms purely Bottom-up strategy 6% or more in the task of shape recognition. Finally, although the CBNs model is designed for visual perception, it has great potential to be applied to other areas as well.

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