Object-level saliency: Fusing objectness estimation and saliency detection into a uniform framework

Most existing saliency detection algorithms concentrate on obtaining good results for images with single salient object, while it produces poor generalization power when tested on more realistic images. In this paper, we present a novel framework to detect saliency in object-level through fusing objectness estimation into the process of salient object detection. Different from most existing methods that evaluate saliency via aggregation of adjacent pixels or regions, our approach peels background regions step by step via evaluating each region's saliency, objectness and background, until all the independent foreground objects are left. Instead of extracting from saliency map, the proposed method can obtain salient objects directly, and different salient scores can be assigned to different salient objects. Experimental results show that the proposed method is effective and achieves state-of-the-art performance in several benchmark datasets, especially on PASCAL_S and SED2 that offer salient objects in more complicated scenes.