Predicting visual attention using gamma kernels

Saliency measures are a popular way to predict visual attention. However, saliency is normally tested on sets of single resolution images that are unlike what the human vision system sees. We propose a new saliency measure based on convolving images with 2D gamma kernels which function as a comparison between a center and a surrounding neighborhood. The two parameters in the gamma kernel provide an ideal way to change the size of both the center and the surrounding neighborhood, which makes finding saliency at different scales simple and fast. We test the new saliency measure on both the CAT2000 database and the Toronto database and compare the results with other simple saliency methods. In addition, we test the methods on a foveated version of the Toronto database to test whether these methods perform well in a fixation system similar to the human vision system. Gamma saliency is shown to both perform better and compute faster than the competing methods in both the standard databases and the foveated version.