Hierarchical-model salient object detection based on manifold ranking

Abstract Salient object detection has attracted a lot of focused research and has resulted in many applications, it is a challenge to detect the most important scene from the input image. Different from most previous methods that only modeled the spatial connectivity of every region is modeled using k-regular graph, and do not consider the deficiency between multi-layer super pixel segmentations based on manifold ranking, we propose a multi-scale approach. First, we tackle an image from a scale point of view and use a multi-scale approach to analyze saliency cues. Second, through building a graph model which is on the basis of k-regular graph, we connect the nodes belonging to the same cluster and located in the same spatial connected area with edges, to highlight the whole goal more uniformly and evenly, then used manifold ranking to generate multi-layer saliency map. Finally, the final saliency map is got through weighted linear fusion. Extensive experiments on six benchmark datasets demonstrate efficiency of the proposed method against the state-of-the-art methods.