Pattern Mining Saliency

This paper presents a new method to promote the performance of existing saliency detection algorithms. Prior bottom-up methods predict saliency maps by combining heuristic saliency cues, which may be unreliable. To remove error outputs and preserve accurate predictions, we develop a pattern mining based saliency seeds selection method. Given initial saliency maps, our method can effectively recognize discriminative and representative saliency patterns (features), which are robust to the noise in initial maps and can more accurately distinguish foreground from background. According to the mined saliency patterns, more reliable saliency seeds can be acquired. To further propagate the saliency labels of saliency seeds to other image regions, an Extended Random Walk (ERW) algorithm is proposed. Compared with prior methods, the proposed ERW regularized by a quadratic Laplacian term ensures the diffusion of seeds information to more distant areas and allows the incorporation of external classifiers. The contributions of our method are complementary to existing methods. Extensive evaluations on four data sets show that our method can significantly improve accuracy of existing methods and achieves more superior performance than state-of-the-arts.