Superpixel-Based Stereoscopic Video Saliency Detection Using Support Vector Regression Learning

Abstract

In this study, a superpixel-based stereoscopic video saliency detection approach is proposed. Based on the input stereoscopic video sequences containing left-view and right-view video sequences, a sequence of right-to-left disparity maps is obtained. First, the simple linear iterative clustering (SLIC) algorithm (Achanta, IEEE Trans Pattern Anal Mach Intell, 34(11):2274–2282, 2012) is used to perform superpixel segmentation on all video frames. Second, the spatial, temporal, depth, object, and spatiotemporal features are extracted from video frames to generate the corresponding feature maps. Third, all feature maps are concatenated and support vector regression (SVR) learning using LIBLINEAR tools is employed to generate the initial saliency maps of video frames. Finally, the initial saliency maps are refined by using the center bias map, the significant increased map, visual sensitivity, and Gaussian filtering. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.