Robust Visual Saliency Optimization Based on Bidirectional Markov Chains

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent saliency detection method based on the bidirectional absorbing Markov chains that jointly exploits not only the boundary information but also the foreground prior and background prior cues. More specifically, the input image is first segmented into number of superpixels, and the four boundary nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node's random walk to the absorbing state is calculated to obtain the foreground possibility. Simultaneously, foreground prior (as the virtual absorbing nodes) is used to calculate the absorption time and get the background possibility. In addition, the two aforementioned results are fused to form a combined saliency map which is further optimized by using a cost function. Finally, the superpixel-level saliency results are optimized by a regularized random walks ranking model at multi-scale. The comparative experimental results on four benchmark datasets reveal superior performance of our proposed method over state-of-the-art methods reported in the literature. The experiments show that the proposed method is efficient and can be applicable to the bottom-up image saliency detection and other visual processing tasks.