Salient object detection based on boundary contrast with regularized manifold ranking

Salient object detection via graph-based manifold ranking, which exploits the boundary prior by using image boundaries as labelled background queries, always achieves impressive performance. However, when the salient object broadly touches the image boundary, this method is fragile and may fail. To address this issue, we present a novel approach which bases on boundary contrast with regularized manifold ranking. First, we compute the contrast saliency against the image boundary as ranking queries, instead of directly using the boundaries as background queries. Second, we use an affinity matrix with regularization for manifold ranking to infer saliency value. Third, we integrate saliency inference result with foregroundness based on boundary connectivity to improve the detection accuracy. Last, we adopt multiscale method to mitigate the object scale effect in saliency detection. Experimental results on three benchmark datasets show that the proposed method achieves comparable or better performance than stat-of-the-art methods.