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http://dx.doi.org/10.3837/tiis.2016.08.020

Visual Attention Model Based on Particle Filter  

Liu, Long (Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, School of Automation and Information Engineering, Xi'an University of Technology)
Wei, Wei (School of Computer Science and Engineering, Xi'an University of Technology)
Li, Xianli (Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, School of Automation and Information Engineering, Xi'an University of Technology)
Pan, Yafeng (Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, School of Automation and Information Engineering, Xi'an University of Technology)
Song, Houbing (Department of Electrical and Computer Engineering, West Virginia University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.8, 2016 , pp. 3791-3805 More about this Journal
Abstract
The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.
Keywords
fusion; particle filter; visual attention model; bayesian filter posterior; resampling;
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