Saliency Detection for Unconstrained Videos Using Superpixel-Level Graph and Spatiotemporal Propagation

This paper proposes an effective spatiotemporal saliency model for unconstrained videos with complicated motion and complex scenes. First, superpixel-level motion and color histograms as well as global motion histogram are extracted as the features for saliency measurement. Then a superpixel-level graph with the addition of a virtual background node representing the global motion is constructed, and an iterative motion saliency (MS) measurement method that utilizes the shortest path algorithm on the graph is exploited to reasonably generate MS maps. Temporal propagation of saliency in both forward and backward directions is performed using efficient operations on inter-frame similarity matrices to obtain the integrated temporal saliency maps with the better coherence. Finally, spatial propagation of saliency both locally and globally is performed via the use of intra-frame similarity matrices to obtain the spatiotemporal saliency maps with the even better quality. The experimental results on two video data sets with various unconstrained videos demonstrate that the proposed model consistently outperforms the state-of-the-art spatiotemporal saliency models on saliency detection performance.