Visual Attention Model Based on Particle Filter

• Long Liu,
• Wei Wei,
• Xianli Li,
• Yafeng Pan,
• Houbing Song,

Vol. 10, No. 8, August 30, 2016
10.3837/tiis.2016.08.020, Download Paper (Free):

• Fusion
• Particle Filter
• visual attention model
• bayesian filter posterior
• resampling

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.

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