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Temporal-Spatial Refinements for Video Concept Fusion

  • Jie Geng
  • Zhenjiang Miao
  • Hai Chi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

The context-based concept fusion (CBCF) is increasingly used in video semantic indexing, which uses various relations among different concepts to refine the original detection results. In this paper, we present a CBCF method called Temporal-Spatial Node Balance algorithm (TSNB). This method is based on a physical model, in which the concepts are regard as nodes and the relations are regard as forces. Then all the spatial and temporal relations and the moving cost of the nodes will be balanced. This method is intuitive and observable to explain a concept how to influence others or be influenced by others. And it uses both the spatial and temporal information to describe the semantic structure of the video. We use TSNB algorithm on the datasets of TRECVid 2005-2010. The results show that this method outperforms all the existed works as we know. Besides, it is faster.

Keywords

Spatial Relation Temporal Relation Semantic Concept Video Shot Concept Detection 
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 2013

Authors and Affiliations

  • Jie Geng
    • 1
  • Zhenjiang Miao
    • 1
  • Hai Chi
    • 2
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Jilin Electric Power Maintenance CompanyChina

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