  In this paper, we model the salient object detection problem under a
probabilistic framework encoding the boundary connectivity saliency cue and
smoothness constraints in an optimization problem. We show that this problem
has a closed form global optimum which estimates the salient object. We further
show that along with the probabilistic framework, the proposed method also
enjoys a wide range of interpretations, i.e. graph cut, diffusion maps and
one-class classification. With an analysis according to these interpretations,
we also find that our proposed method provides approximations to the global
optimum to another criterion that integrates local/global contrast and large
area saliency cues. The proposed approach achieves mostly leading performance
compared to the state-of-the-art algorithms over a large set of salient object
detection datasets including around 17k images for several evaluation metrics.
Furthermore, the computational complexity of the proposed method is
favorable/comparable to many state-of-the-art techniques.
