Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition

Abstract

In this paper, we propose a trainable selective attention model that can inhibit an unwanted salient area and only focus on an interesting area in a static natural scene. The proposed model was implemented by the bottom-up saliency map model in conjunction with the modified adaptive resonance theory (ART) network model. The bottom-up saliency map model generates a salient area based on intensity, edge, color, and symmetry feature maps, and a human supervisor decides whether the selected salient area is important. If the selected area is not interesting, the ART network trains and memorizes that area, and also generates an inhibit signal so that the bottom-up saliency map model does not pay attention to an area with similar characteristic in subsequent visual search process. Computer simulation results show that the proposed model successfully generates a plausible sequence of salient regions that does not include unwanted areas.