Video co-segmentation based on directed graph

Yufeng Xie, Zhi Liu, Xiaofei Zhou, Wei Liu, Xuemei Zou

Abstract: This paper proposes a novel video co-segmentation method, which aims to extract multi-class objects from a group of videos. A set of tracklets are first generated based on object proposals, and then a novel directed graph is constructed to connect object tracklets. The directed graph is transformed to an undirected graph, and the extraction of common object tracklets is solved by using maximum weighted clique. The obtained common object tracklets are used as seed regions to perform manifold ranking and to generate the object-level saliency maps. Based on common object tracklets and object-level saliency maps, GrabCut is exploited to get the refined co-segmentation results. Experimental results on a public video dataset show that the proposed video co-segmentation method consistently outperforms the state-of-the-art methods.