Automatic salient object extraction with contextual cue and its applications to recognition and alpha matting

A method for automatically extracting salient object from a single image is presented in this paper. The proposed method is cast in an energy minimization framework. Unlike that only appearance cues are leveraged in most previous methods, an auto-context cue is used as a complementary data term. Benefitting from a generic saliency model for bootstrapping, the segmentation of the salient object and the learning of the auto-context model are iteratively performed without any user intervention. Upon convergence, the method outputs not only a clear separation of the salient object, but also an auto-context classifier which can be used to recognize the same type of object in other images. Our experiments on four benchmarks demonstrated the efficacy of the added contextual cue. It is shown that our method compares favorably with the state-of-the-art, some of which even embraced user interactions. Furthermore, we present some initial recognition results from the induced auto-context model and also show that the segmentation produced by our approach could serve as a good initialization for alpha matting.