Salient object detection with higher order potentials and learning affinity
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Salient object detection with higher order potentials and learning affinity
- 1. SALIENT OBJECT DETECTION WITH HIGHER ORDER POTENTIALS AND LEARNING AFFINITY ABSTRACT In this paper, we propose a novel graph-based salientobject detection algorithm which exploits higher order potential tocapture the cross-scale grouping cues instead of using multi- scalegraph model or naive multi-scale fusion (i.e. individually computea saliency result for each scale and then combine them). And, weinvestigate the importance of graph affinities in graph labeling. Web take both local (spatial distribution) and nonlocal (feature distribution)priors into account and learn the pairwise similarity valuesin a semi-supervised manner, thereby obtaining a faithful graphaffinity model. With the guidance of foreground and backgroundseeds, salient object detection is formulated as a labeling inferenceproblem. Extensive experiments on two large benchmark datasetsdemonstrate the proposed method performs well when against thestate- of-the-art methods in terms of accuracy.