A model of attention-guided visual sparse coding

Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) compete for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model. This model includes two modules: nonuniform sampling module simulating the process of retina and; data-driven attention module based on the response saliency. Our experiment results show that the model notably decreases the number of coefficients which may be activated and retains the main vision information at the same time.