A novel graph structure for salient object detection based on divergence background and compact foreground

In this paper, we propose an efficient and discriminative model for salient
object detection. Our method is carried out in a stepwise mechanism based on
both divergence background and compact foreground cues. In order to effectively
enhance the distinction between nodes along object boundaries and the
similarity among object regions, a graph is constructed by introducing the
concept of virtual node. To remove incorrect outputs, a scheme for selecting
background seeds and a method for generating compactness foreground regions are
introduced, respectively. Different from prior methods, we calculate the
saliency value of each node based on the relationship between the corresponding
node and the virtual node. In order to achieve significant performance
improvement consistently, we propose an Extended Manifold Ranking (EMR)
algorithm, which subtly combines suppressed / active nodes and mid-level
information. Extensive experimental results demonstrate that the proposed
algorithm performs favorably against the state-of-art saliency detection
methods in terms of different evaluation metrics on several benchmark datasets.