MINING ACTOR CORRELATIONS WITH HIERARCHICAL CONCURRENCE PARSING

Mining actor correlations from TV series enables semantic-level video understanding and facilitates users to conduct correlation-based query. In this paper, we introduce a graph-based actor correlations mining framework, which serves as the first attempt for effective actor association presentation and concurrence search. We leverage face detection and tracking to locate actors with 2D-PCA detector as pretreatment. To measure the actor association into a unified graph, we propose a context-based actor correlations hierarchical parsing approach, which considers video structure and hierarchical concurrence to refine actor association in our graph modeling. We not only can accomplish actor correlations mining, but also can acquire higher semantic information according to concurrence change. We present the actor correlation mining results in a graph-based interface to enable efficient users' navigation and search.