A biological inspired features based saliency map

A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns. © 2012 IEEE