Measuring visual saliency by Site Entropy Rate
In this paper, we propose a new computational model for visual saliency derived from the information maximization principle. The model is inspired by a few well acknowledged biological facts. To compute the saliency spots of an image, the model first extracts a number of sub-band feature maps using learned sparse codes. It adopts a fully-connected graph representation for each feature map, and runs random walks on the graphs to simulate the signal/information transmission among the interconnected neurons. We propose a new visual saliency measure called Site Entropy Rate (SER) to compute the average information transmitted from a node (neuron) to all the others during the random walk on the graphs/network. This saliency definition also explains the center-surround mechanism from computation aspect. We further extend our model to spatial-temporal domain so as to detect salient spots in videos. To evaluate the proposed model, we do extensive experiments on psychological stimuli, two well known image data sets, as well as a public video dataset. The experiments demonstrate encouraging results that the proposed model achieves the state-of-the-art performance of saliency detection in both still images and videos.