A novel semantic model for video concept detection

Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of training samples in many real-world application areas, such as video annotation. As a significant factor of these algorithms, however, pairwise similarity metric has not been fully investigated. On the one hand, for general video annotation methods, the estimation of pairwise similarity between two samples relies on the spatial property of video data. On the other hand, temporal property, which is an essential character of video data, is not embedded into the pairwise similarity metric. Therefore, in this paper, a novel method, called joint spatio-temporal correlation learning (JSTCL), is proposed to improve the accuracy of video annotation. This method is characterized by simultaneously taking into account both the spatial and temporal property of video data to well represent the pairwise similarity. Experiments conducted on the TRECVID demonstrate the efficiency of the proposed method.