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Application of Bayesian Inference to Automatic Semantic Annotation of Videos

  • Fangshi Wang
  • De Xu
  • Hongli Xu
  • Wei Lu
  • Weixin Wu
Part of the Studies in Computational Intelligence book series (SCI, volume 64)

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.

Keywords

Bayesian Network Bayesian Inference Average Precision Semantic Network Semantic Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fangshi Wang
    • 1
    • 2
  • De Xu
    • 1
  • Hongli Xu
    • 1
  • Wei Lu
    • 2
  • Weixin Wu
    • 1
  1. 1.School of Computer & Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.School of SoftwareBeijing Jiaotong UniversityBeijingChina

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