Saliency detection via foreground rendering and background exclusion

In this paper, a novel approach for image visual saliency detection is proposed from both the salient object (foreground) and the background perspective. To better highlight the salient object, we start from what is a salient object and adopt priors including contrast prior and center prior to measure the dissimilarity between different image elements. To better suppress the background, we focus on what is the background and measure the pixel-wise saliency by the minimum seam cost where the seam is an optimal 8-connected path from the pixel to some boundary pixel. The final saliency map is obtained by the combination of two measure systems which leads to the goal of both highlighting the salient object and suppressing the background. Both qualitative and quantitative experiments conducted on a benchmark dataset show that our approach outperforms seven state-of-the-art methods.