Probabilistic Saliency Estimation
This work addresses the problem of accurately detecting salient objects in images for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles salient object detection by modeling it under a probabilistic framework with boundary connectivity and smoothness constraints, achieving a closed-form global optimum and leading performance on datasets with around 17k images across several metrics.
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.