Graph-Based Saliency Fusion with Superpixel-Level Belief Propagation for 3D Fixation Prediction

In recent years, many 3D visual attention models (VAMs) have been proposed with diverse fusion methods, of which the main challenge lies in the inconsistence, or even conflicts of different saliency maps. To address the challenge, we propose a graph-based fusion method with superpixel-level belief propagation for 3D fixation prediction on stereoscopic video, which models the aggregation as a global optimization issue. After extracting multi-modality saliency maps, the fusion step is based on the graph constructed at superpixel level, and we design for the graph an energy function considering multi-modality constraints, which is minimized using the belief propagation algorithm. The experimental results on two databases demonstrate that the proposed model achieves competitive performance.