Mining representative actions for actor identification

Previous works on actor identification mainly focused on static features based on face identification and costume detection, without considering the abundant dynamic information contained in videos. In this paper, we propose a novel method to mine representative actions of each actor, and show the remarkable power of such actions for actor identification task. Videos are firstly divided into shots and represented by BoW based on spatial-temporal features. Then we integrate the prototype theory with SVM to rank the shots and obtain the representative actions. Our method for actor identification combines representative actions with actors' appearance. We validate the method on episodes of the TV series "The Big Bang Theory". The experimental results show that the representative actions are consistent with human judgements and can greatly improve the matching performance as complementary to existing handcrafted static features for actor identification.