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Retrieving videos using key words requires obtaining the semantic features of the videos. Most work reported in the literature focuses on annotating a video shot with a fixed number of key words, no matter how much information is contained in the video shot. In this paper, we propose a new approach to automatically annotate a video shot with an adaptive(More)
In this paper, a multi-feature max-margin hierarchical Bayesian model (M<sup>3</sup>HBM) is proposed for action recognition. Different from existing methods which separate representation and classification into two steps, M<sup>3</sup>HBM jointly learns a high-level representation by combining a hierarchical generative model (HGM) and discriminative(More)
There is more and more video information on the web. The recognition of semantic information from visual content is an important task in video retrieval. Semantic network which captures the semantic relationships among concepts can be used for video annotation. In this paper, we present an improved three-phase dependency analysis (ITPDA) algorithm(More)
The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local(More)