Abstract
Visual attention modeling is crucial for interpreting the structure and functionality of human vision system. A typical computational model of visual attention includes two basic elements: visual representation and saliency measurement. Most existing models left two phases unmodifiable without explicit adaption to the statistics of their corresponding visual environment. Inspired by neural adaption of biological neural systems, we proposed a novel principle for modeling visual attention mechanism named short-term environmental adaption. Given the statistics of a specified short-term visual environment, the proposed model adaptively extract sparse features and treats saliency as the features’ conditional self-information, which is more accurate in saliency measurement and more sparse with respect to visual signal representation.We have demonstrated our superior effectiveness and robustness over state-of-the-arts by carrying out dense experiments on human eye fixation benchmarks as well as psychological patterns.
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