Biologically Inspired Saliency Map Model for Bottom-up Visual Attention

In this paper, we propose a new saliency map model to find a selective attention region in a static color image for human-like fast scene analysis. We consider the roles of cells in our visual receptor for edge detection and cone opponency, and also reflect the roles of the lateral geniculate nucleus to find a symmetrical property of an interesting object such as shape and pattern. Also, independent component analysis (ICA) is used to find a filter that can generate a salient region from feature maps constructed by edge, color opponency and symmetry information, which models the role of redundancy reduction in the visual cortex. Computer experimental results show that the proposed model successfully generates the plausible sequence of salient region.