Salient object detection via contrast information and object vision organization cues

As a popular topic, saliency detection has attracted lots of research interest benefiting for its valuable applications in computer vision and image processing. In this paper, we propose to delineate saliency by considering both the contrast and object vision organization. It consists of two stages. In the first stage, the primary element, contrast saliency, is acquired by measuring color contrast and color distribution with background prior and center prior to address the uniqueness and compactness of salient regions. In the second stage, inspired by the Gestalt principles of grouping from the study of visual perception, we take into account the properties of closure, proximity and similarity for object vision organization, and then provide the object vision saliency filtering to emphasize homogeneous saliency across similar and object-like regions. As for the task, a map called object coverage confidence is presented to express the closure by characterizing the probability of complete object areas with refined profiles, which is constructed by fusing multiple information prediction maps, implying probable closure areas of objects in different layers of an image. Experimental results on five publicly available benchmarks demonstrate that our model outperforms the state-of-the-art methods.