Saliency optimization via k-means clustering and low rank matrix recovery

In the field of image saliency detection, previous approaches are mostly built on the low level priors like color and space contrast, boundary or connectivity prior, which are not sufficient to differentiate real salient regions from other independent or high-contrast parts. In this paper, we propose a novel approach which utilizes both high level and low level priors or cues to generate a salient map. Specifically, we obtain a foreground likelihood map via low rank matrix recovery, which incorporates traditional low-level features with higher-level guidance. Then, we compute a background likelihood map via principal component analysis and k-means clustering, which utilize two low level priors: distribution and boundary priors. Finally, the salient values of one image are calculated based on the two likelihood maps via a modified optimization framework. Extensive experiments on numerous publicly available datasets demonstrate that our proposed algorithm outperforms state-of-the-art methods.