A biologically inspired computational model for image saliency detection

Image saliency detection provides a powerful tool for predicting where human tends to look at in an image, which has been a long attempt for the computer vision community. In this paper, we propose a biologically-inspired model for computing image saliency. At first, a set of basis functions that accords with visual responses to natural stimuli is learned by using eye-fixation patches from an eye-tracking dataset. Three features are then derived based on the learned basis functions including continuity, clutter contrast, and local contrast. Finally, these three features are combined into the saliency map. The proposed approach is easy to implement and can be used in many image and video content analysis applications. Experiments on a large-scale benchmark dataset and comparisons with a number of the state-of-the-art approaches demonstrate its superiority.