Toward adaptive fusion of multiple cues for salient region detection

Boundary priors have been extensively studied in salient region detection problems over the past few decades. Although several models based on the boundary prior have achieved good detection performance, there still exist drawbacks. The most common one is that they fail to detect the salient object when the background is complex or the salient object touches the image boundary. In this paper, we propose a novel model to detect the salient region. It is based on background cues and one complementary cue, that is, a foreground cue. A saliency score is obtained via solving an energy optimization problem which takes both the background cue and foreground cue into consideration. Extensive experiments, including both quantitative and qualitative evaluations on five widely used datasets, demonstrate the superiority of our proposed model to several other state-of-the-art models.