Spatio-temporal saliency detection using abstracted fully-connected graphical models
Amir-Hossein Karimi, Mohammad Javad Shafiee, Christian Scharfenberger, Ibrahim Ben-Daya, Shahid Abbas Haider, Nur Alom Talukdar, David A. Clausi, Alexander K.C. Wong · 2016
A novel approach to spatio-temporal saliency detection in video is proposed. Saliency computation is considered as an optimization problem that maximizes the energy of a fully-connected graphical model based on spatio-temporal feature distinctiveness. Each pixel in a video is modeled by a node, and the spatio-temporal feature distinctiveness between pixels by edges connecting the nodes in the graph. The computational complexity is addressed by compressing the fully-connected graph into an abstracted, fully-connected graph with far fewer nodes, where each node in the new graph characterizes nodal groups. The saliency value of each pixel is then computed based on spatio-temporal feature distinctiveness and the energy representation of its nodal group given the constructed graphical model. Experimental results show that our approach outperforms existing approaches to spatio-temporal salient region detection.