Saliency detection using two-stage scoring

In this paper, we propose a novel saliency detection approach, which is robust to images with complex background. In our algorithm, an intuitive and straightforward pre-treatment method is formulated for conducting over-segmentation adaptively. To detect saliency effectively, a two-stage scoring method is adopted, in which both background prior and foreground cues are considered. In the first stage, we conduct random walk on absorbing Markov chain with background prior. And in the second stage, we use the saliency scores computed by the first stage scoring as foreground cues for manifold ranking. Experimental results on publicly available datasets demonstrate that our method outperforms the state-of-the-art methods in detecting salient objects.