Saliency Detection Based On Markov Absorption Probabilities

Visual saliency detection is a significant and challenging task in computer vision field. Effective saliency detection models have been applied to numerous computer vision scenarios, such as objects-of-interest segmentation, object classification and recognition, content aware image resizing, and so forth. Therefore, salient object detection have attracted much attention by more and more researchers in recent years. The main task of saliency detection algorithm is to estimate the position of the salient region in an image, and obtain a gray map where the intensity of the pixel indicates the possibility of being to the salient region.In this paper, we present a 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 saliency region detection 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 three publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against seventeen state-of-the-art methods.