Image Saliency Detection via Graph Representation with Fusing Low-level and High-level Features

To employ complementary benefits of different level features effectively and improve the robustness, we propose a graph representation based image saliency detection method, which fuses low-level and high-level features. We take superpixels as graph nodes to construct the graph model, in which the weights of the nodes and edges are defined by high-level features and the difference of low-level features, respectively. Then, a symmetric transition probability matrix is constructed based on the proposed graph representation model, and the Markov random walk algorithm is utilized to optimize this model and obtain the initial saliency map. To improve the robustness of the proposed method, the center prior and the improved boundary prior are integrated into our model. Extensive experiments on four publicly available datasets with ten approaches demonstrate the effectiveness of the proposed approach.