Non-local spatial redundancy reduction for bottom-up saliency estimation
In this paper we present a redundancy reduction based approach for computational bottom-up visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The result redundancy coefficient is used to compensate the Shannon entropy, which is based on statistics of pixel intensities, to generate the bottom-up saliency map of the visual input. Experimental results on three publicly available databases demonstrate that the proposed model is highly consistent with the subjective visual attention. (c) 2012 Elsevier Inc. All rights reserved.
non-local redundancy reduction bottom-up visual saliency self-similarity entropy visual attention image structure human visual system