Salient object detection via global contrast graph
Fatemeh Nouri, Kamran Kazemi, Habibollah Danyali · 2015
In this paper, we propose an unsupervised bottom-up method which formulates salient object detection problem as finding salient vertices of a graph. Global contrast is extracted in a novel graph-based framework to determine localization of salient objects. Saliency values are assigned to regions in terms of nodes degrees on graph. The proposed method has been applied on SED2 dataset. The qualitative and quantitative evaluation of the proposed method show that it can detect the salient objects appropriately in comparison with 5 state-of-art saliency models.