Summary. It is an important task to automatically extract semantic annotation of a video shot. This high level semantic information can improve the performance of video retrieval. In this paper, we propose a novel approach to annotate a new video shot automatically with a non-fixed number of concepts. The process is carried out by three steps. Firstly, the semantic importance degree (SID)is introduced and a simple method is proposed to extract the semantic candidate set (SCS) under considering SID of several concepts co-occurring in the same shot. Secondly, a semantic network is constructed using an improved K2 algorithm. Finally, the final annotation set is chosen by Bayesian inference. Experimental results show that the performance of automatically annotating a new video shot is significantly improved using our method, compared with classical classifiers such as Naïve Bayesian and K Nearest Neighbor.
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Wang, F., Xu, D., Xu, H., Lu, W., Wu, W. (2007). Application of Bayesian Inference to Automatic Semantic Annotation of Videos. In: Magnani, L., Li, P. (eds) Model-Based Reasoning in Science, Technology, and Medicine. Studies in Computational Intelligence, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71986-1_26
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DOI: https://doi.org/10.1007/978-3-540-71986-1_26
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