A Novel Indexing Approach for Efficient and Fast Similarity Search of Captured Motions

  • Chuanjun Li
  • B. Prabhakaran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Indexing of motion data is important for quickly searching similar motions for sign language recognition and gait analysis and rehabilitation. This paper proposes a simple and efficient tree structure for indexing motion data with dozens of attributes. Feature vectors are extracted for indexing by using singular value decomposition (SVD) properties of motion data matrices. By having similar motions with large variations indexed together, searching for similar motions of a query needs only one node traversal at each tree level, and only one feature needs to be considered at one tree level. Experiments show that the majority of irrelevant motions can be pruned while retrieving all similar motions, and one traversal of the indexing tree takes only several microseconds with the existence of motion variations.


Feature Vector Singular Value Decomposition Leaf Node Motion Data Singular Vector 
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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  1. 1.
    Golub, G.H., Loan, C.F.V.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)MATHGoogle Scholar
  2. 2.
    Kahveci, T., Singh, A., Gurel, A.: Similarity searching for multi-attribute sequences. In: Proceedings of 14th Int’l Conference on Scientific and Statistical Database Management, July 2002, pp. 175–184 (2002)Google Scholar
  3. 3.
    Korn, F., Jagadish, H.V., Faloutsos, C.: Efficiently supporting ad hoc queries in large datasets of time sequences. In: SIGMOD, May 1997, pp. 289–300 (1997)Google Scholar
  4. 4.
    Lee, S.-L., Chun, S.-J., Kim, D.-H., Lee, J.-H., Chung, C.-W.: Similarity search for multidimensional data sequences. In: Proceedings of 16th Int’l Conference on Data Engineering, Febraury/March 2000, pp. 599–608 (2000)Google Scholar
  5. 5.
    Li, C., Prabhakaran, B.: A similarity measure for motion stream segmentation and recognition. In: Proceedings of the Sixth International Workshop on Multimedia Data Mining, August 2005, pp. 89–94 (2005)Google Scholar
  6. 6.
    Li, C., Pradhan, G., Zheng, S., Prabhakaran, B.: Indexing of variable length multiattribute motion data. In: Proceedings of the Second ACM International Workshop on Multimedia Databases 2004, November 2004, pp. 75–84 (2004)Google Scholar
  7. 7.
    Schutter, B.D., Moor, B.D.: The singular value decomposition in the extended max algebra. Linear Algebra and Its Applications 250, 143–176 (1997)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Online Vicon products introduction, http://www.vicon.com/jsp/products/products.jsp
  9. 9.
    Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional time-series with support for multiple distance measures. In: SIGMOD, August 2003, pp. 216–225 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chuanjun Li
    • 1
  • B. Prabhakaran
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA

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