Salient object detection with high-level prior based on Bayesian fusion

Most of approaches to salient object detection focused on two-dimensional images, while rare attention was attached to the light field which can provide exclusive visual information for salient object detection and other computer vision applications. An effective algorithm of salient object detection is proposed for light field data. First, boundary connectivity is calculated on all-focus image. Then, background probability based on boundary connectivity is achieved by computing geodesic distance. Second, the authors rank the similarity of the superpixels of both all-focus image and depth map via graph-based manifold ranking to carry out two initial saliency maps. Third, weighted by background probability, the two initial saliency maps are fused to produce final saliency results, integrated by objectness cue. The authors also exploit how to integrate effectively objectness with other visual features, and compare two fusion strategies: linear fusion and Bayesian integration. Experiments show that light field features are helpful for saliency detection, and Bayesian integration framework is the better choice than linear fusion method. Meanwhile, the way how to combine multiple features is crucial. The proposed algorithm handles challenging natural scenarios such as cluttered background, similar foreground and background, and so on, and produces visual favourable results in comparison with the eight state-of-the-art methods.