Fangshi Wang

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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)
Annotating videos manually is very costly and time consuming. Human being's subjective and different understanding often lead to incomplete and inconsistent annotations and poor system performance. So it is an importance topic to annotate automatically semantic concepts for a video. Discovering the relationships among several concepts coexisting in the same(More)
Annotating videos manually is very costly and time consuming. Human being's subjective and different understanding often lead to incomplete and inconsistent annotations and poor system performance. So it is an important topic to automatically annotate a video shot. In this paper, we propose a new approach of automatically extracting a non-fixed number of(More)