Active visual computing model based on data- and knowledge- driven selective attention mechanism

Strong evidence has shown that visual processing based on selective attention is both data- and knowledge- driven. However, most of the previous work mainly focused on the former. We propose in this paper a new selective attention visual computing model based on both of them. The novelty lies in: (1) A structure variable non-uniform sampling method is proposed to separate visual computing into foveal and peripheral channel. (2) A combination of the bottom-up and the top-down selective attention mechanism based on a two-layered pyramid is proposed. The data-driven bottom-up selective attention includes the sequential extraction of feature maps, conspicuity maps, and interesting map based on the multi-channel filtering and relaxation process. The knowledge driven top-down selective attention is based on distributed associative memory mapping. (3) A movement control mechanism is also proposed in this paper. Perfectly good experiment results on artificial and real images demonstrate the validity of our model.