Saliency Region Detection Based on Markov Absorption Probabilities

In this paper, we present a novel bottom-up salient object detection approach by exploiting the relationship between the saliency detection and the Markov absorption probability. First, we calculate a preliminary saliency map by the Markov absorption probability on a weighted graph via partial image borders as background prior. Unlike most of the existing background prior-based methods which treated all image boundaries as background, we only use the left and top sides as background for simplicity. The saliency of each element is defined as the sum of the corresponding absorption probability by several left and top virtual boundary nodes, which are most similar to it. Second, a better result is obtained by ranking the relevance of the image elements with foreground cues extracted from the preliminary saliency map, which can effectively emphasize the objects against the background, whose computation is processed similarly as that in the first stage and yet substantially different from the former one. At last, three optimization techniques - content-based diffusion mechanism, superpixelwise depression function, and guided filter - are utilized to further modify the saliency map generalized at the second stage, which is proved to be effective and complementary to each other. Both qualitative and quantitative evaluations on four publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against 17 state-of-the-art methods.