In recent years, many different proposals for visual saliency computation have been put forth, that generally frame the determination of visual saliency as a measure of local feature contrast. There is however, a paucity of approaches that take into account more global holistic elements of the scene. In this paper, we propose a novel mechanism that augments the visual representation used to compute saliency. Inspired by research into biological vision, this strategy is one based on the role of recurrent computation in a visual processing hierarchy. Unlike existing approaches, the proposed model provides a manner of refining local saliency based computation based on the more global composition of a scene that is independent of semantic labeling or viewpoint. The results presented demonstrate that a fast recurrent mechanism significantly augments the determination of salient regions of interest as compared with a purely feed forward visual saliency architecture. This demonstration is applied to the problem of detecting targets of interest in various surveillance scenarios. © 2012 IEEE.
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