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An Evaluation Method for Video Semantic Models

  • Yu Wang
  • Lizhu Zhou
  • Chunxiao Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3665)

Abstract

The development of video technology and video-related applications demands strong support in semantic data models. To meet such a requirement, many video semantic data models have been proposed. The semantic model plays a key role in providing query capability and other features for a video database. However, to our knowledge, the criteria for a good semantic model remain open at present. As a result, people lack the rules for evaluating an existing model and the guidelines for the design of a new data model when necessary. To address this issue, this paper proposes twenty one properties as the criteria for video semantic models, and gives the evaluation result of eleven existing rich semantic models according to these properties. It shows that these models mostly concentrate on basic expressive power and query capability, and fulfill users’ primary requirements. But in some advanced features such as expressive power, acquisition and analysis of semantic information, and query capability etc., there are rooms for further enhancement. The paper concludes by indicating some research directions for video semantic models.

Keywords

Semantic Information Video Data Query Language Semantic Model Expressive Power 
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 2005

Authors and Affiliations

  • Yu Wang
  • Lizhu Zhou
  • Chunxiao Xing

There are no affiliations available

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