Content-Based Human Motion Retrieval with Automatic Transition

  • Yan Gao
  • Lizhuang Ma
  • Yiqiang Chen
  • Junfa Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


This paper presents a framework for efficient content-based motion retrieval. To bridge the gap between user’s vague perception and explicit motion scene description, we propose a Scene Description Language that can translate user’s input into a series of set operations between inverted lists. Our Scene Description Language has three-layer structures, each describing scenes at different levels of granularity. By introducing automatic transition strategy into our retrieval process, our system can search motions that do not exist in a motion database. This property makes our system have potentials to serve as motion synthesis purpose. Moreover, by using various kinds of qualitative features and adaptive segments of motion capture data stream, we obtain a robust clustering that is flexible and efficient for constructing motion graph. Some experimental examples are given to demonstrate the effectiveness and efficiency of proposed algorithms.


Motion Segment Motion Type Automatic Transition Union Operation Inverted List 
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 2006

Authors and Affiliations

  • Yan Gao
    • 1
  • Lizhuang Ma
    • 1
  • Yiqiang Chen
    • 2
  • Junfa Liu
    • 2
  1. 1.Department of Computer Science & EngineeringShanghai Jiao Tong UniversityShanghaiP.R.C
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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