Video attention: Learning to detect a salient object sequence

We study video attention by detecting a salient object sequence from video segment. We formulate salient object sequence detection as energy minimization problem in a conditional random field framework, while static and dynamic salience, spatial and temporal coherence, global topic model are well defined and integrated to identify a salient object sequence. Dynamic programming algorithm is designed to resolve a global optimization, with a rectangle to represent each salient object. We validate our approach on a large number of video segments with the labeled salient object sequence.