Hierarchical Bayesian modelling of visual attention

The brain employs interacting bottom-up and top-down processes to speed up searching and recognizing visual targets relevant to specific behavioral tasks. In this paper, we proposed a Bayesian model of visual attention that optimally integrates top-down, goal-driven attention and bottom-up, stimulus-driven visual saliency. In this approach, we formulated a multi-scale hierarchical model of objects in natural contexts, where the computing nodes at the higher levels have lower resolutions and larger sizes than the nodes at the lower levels, and provide local contexts for the nodes at the lower levels. The conditional probability of a visual variable given its context is calculated in an efficient way. The model entails several existing models of visual attention as its special cases. We tested this model as a predictor of human fixations in free-viewing and object searching tasks in natural scenes and found that the model performed very well.