Salient object detection based on distribution-edge guidance and iterative Bayesian optimization

Salient object detection has witnessed rapid progress, despite most existing methods still struggling in complex scenes, unfortunately. In this paper, we propose an efficient framework for salient object detection based on distribution-edge guidance and iterative Bayesian optimization. By considering color, spatial, and edge information, a discriminative metric is first constructed to measure the similarity between different regions. Next, boundary prior embedded with background scatter distribution is utilized to yield the boundary contrast map, and then a contour completeness map is derived through a wholly closed shape of the object. Finally, the above both maps are jointly integrated into an iterative Bayesian optimization framework to obtain the final saliency map. Results from an extensive number of experimentations demonstrate that the promising performance of the proposed algorithm against the state-of-the-art saliency detection methods in terms of different evaluation metrics on several benchmark datasets.