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In this paper, we propose an efficient and discriminative model for salientobject detection. Our method is carried out in a stepwise mechanism based onboth divergence background and compact foreground cues. In order to effectivelyenhance the distinction between nodes along object boundaries and thesimilarity among object regions, a graph is constructed by introducing theconcept of virtual node. To remove incorrect outputs, a scheme for selectingbackground seeds and a method for generating compactness foreground regions areintroduced, respectively. Different from prior methods, we calculate thesaliency value of each node based on the relationship between the correspondingnode and the virtual node. In order to achieve significant performanceimprovement consistently, we propose an Extended Manifold Ranking (EMR)algorithm, which subtly combines suppressed / active nodes and mid-levelinformation. Extensive experimental results demonstrate that the proposedalgorithm performs favorably against the state-of-art saliency detectionmethods in terms of different evaluation metrics on several benchmark datasets.

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