Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking

As an important task in the process of image understanding and analysis, saliency detection has recently received increasing attention. In this paper, we propose an efficient multiple self-weighted graph-based manifold ranking method to construct salient maps. First, we extract several different views of features from superpixels, and generate original salient regions as foreground and background cues using boundary information via multiple graph-based manifold ranking. Furthermore, a set of hyperparameters is learned to distinguish the importance between different graphs, which can be viewed as an adaptive weighting of each graph, and then a centroid graph is generated by using these self-weighted multiple graphs. An iterative algorithm is proposed to simultaneously optimize the hyperparameters as well as the centroid graph connection. Thus, an ideal centroid graph can be obtained, offering a more clear profile of the separated structure. Finally, the saliency maps can be produced with an approximate binary image from the manifold ranking. Extensive experiments have demonstrated our method consistently achieves superior detection performance than several state-of-the-arts.