Graph model-based salient object detection using objectness and multiple saliency cues

Abstract Recent years have witnessed increasing interest in salient object detection, which aims at stimulating the human visual attention mechanism to detect and segment the most attractive object in natural scenes, and can be widely applied in numerous computer vision tasks. In this paper, by considering both objectness cue and saliency detection, we propose a graph model-based bottom-up salient object detection framework by fusing multiple saliency maps using low-level features and objectness features under a manifold ranking framework. Specifically, for each feature, we utilize geodesic distance between any two superpixels to construct the affinity matrix and un-normalized Laplacian matrix of the graph. Then, we apply saliency optimization to refine each saliency map generated by manifold ranking with the first-stage query, and integrate saliency maps corresponding to different features by multilayer cellular automata in the final stage. Extensive experimental results demonstrate that our method can deliver promising performance in comparison to several state-of-the-art bottom-up methods on many benchmark datasets.