A robust 3D visual saliency computation model for human fixation prediction of stereoscopic videos

3D saliency have been gaining an increasing amount of attention because of the emergence of 3D contents and applications. Most of existing works on 3D saliency are based on the assumption of fine quality of depth maps. However, depth maps from stereo matching or range sensors are usually with holes and artifacts, which severely drop the performance of those 3D saliency models. In this paper, we propose a robust 3D saliency computation model for human fixation prediction. First, a cluster-contrast saliency prediction model is proposed for depth maps. The prediction is obtained with the centroid of the largest clusters of each depth super-pixel, and thus those bad effects originate from holes and other artifacts in depth are then eliminated. The cluster-contrast strategy is exploited both to depth texture and motions in 3D video. Finally, a Bayesian integration model is proposed for the multi-modality fusion between depth saliency and color saliency. The experimental results demonstrate that our saliency model has better performance of accuracy than other state-of-arts models.