关键词: 协同显著性检测, 交叉图像显著性传播, 图像内显著性约束, 凸包先验机制

Abstract: Co-saliency detection refers to finding common salient foreground region in a set of related images. The existing method captures the relationship of node pairs in the image, and uses human prior knowledge to construct a co-saliency detection model. However, the high-order relationship between the nodes in the detection is neglected, and the potential connection between the nodes is not mined, resulting in a suboptimal co-saliency map. This paper proposes a novel hypergraph co-saliency propagation detection framework for salient seed propagation. Specifically, the framework consists of inter saliency propagation and intra saliency constraints. The former uses salient seed point based on single saliency map and cross-image cross-propagation mechanism. The fusion algorithm detects the co-salient objects of the image group and suppresses non-co-salient object. The object obtains the preliminary co-saliency map. The later introduces the convex hull prior mechanism of the image, learns the spatial distribution information, constrains the common background noise, suppresses the similar background area, and obtains a more accurate co-saliency map. A large number of experiments are carried out on two widely used co-saliency detection datasets. The results show that as the same unsupervised model, compared with the existing unsupervised co-saliency detection method, this paper obtains the optimal performance.

Key words: co-saliency detection, inter saliency propagation, intra saliency constraint, convex hull prior