An effective visual saliency detection method based on maximum entropy random walk
Visual saliency detection (VSD) has been attracting increasing attention due to its wide applications in computer visions. In this paper, a visual saliency detection method based on maximum entropy random walk (MEVSD) is proposed. Gaze wandering over images is modeled as a random walk process on a graph, in which the super pixels and their similarities are regarded as nodes and edges respectively. The visual salient region is detected based on the stationary distribution of the stochastic process. Compared with the stat of the art methods, MEVSD focuses on globally maximizing the entropy of walking trajectories. A two tier neighbor scheme of super pixels is proposed to reduce the computational complexity. Besides, a belief diffusion algorithm on weight matrix is proposed to improve precision rate. Extensive experiments with diverse content of pictures indicate that MEVSD performs well compared with 11 state of the art methods.
2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW)