Salient region detection by learning accurate background template

In this paper, a salient region detection algorithm based on priors which estimate the likely position of background which we make it more accurate, is proposed. Instead of considering the contrast between each element (pixel or region) and their surrounding regions, we consider background cues in different ways such as PCA and sparse representation. Then the saliency value of each element (superpixel) is obtained by computing the similarity with background cues. Considering such a problem which the background template based on prior probably includes the interference of foreground, so we use graph-based manifold ranking method to eliminate the interference of foreground in background template. In a detailed experimental evaluation on large benchmark database, the results shows the proposed algorithm perform well when compare with other state-of-the-art methods and can effectively eliminate the interference of foreground in initial background templates.