Robust pixelwise saliency detection via progressive graph rankings

Abstract

In this paper, we propose a novel saliency detection method based on superpixel-to-pixel level optimization. First, we segment the input image into superpixels under four scales. For each scale, we construct a k-regular basic graph with these superpixels as nodes. Furthermore, we enlarge the basic graph with virtual absorbing nodes and utilize absorbing Markov chain ranking to calculate background-based saliency. Second, for each scale, we obtain robust foreground queries from the previous result, and use manifold ranking to obtain foreground-based saliency. Third, a regularized random walk ranking based on the pixelwise graph for each scale is used to diffuse the saliency values among pixels. Finally, we obtain four saliency maps for the input image and integrate them together for the final saliency map. Extensive experiments on several challenging datasets reveal that the proposed method performs better in terms of precision, recall and F-measure values. Despite complex backgrounds, our method performs better in detecting small and/or multiple salient objects than other state-of-the-art methods as a whole.