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An Efficient Algorithm for Keyframe-based Motion Retrieval in the Presence of Temporal Deformations

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44051

Baak,  Andreas
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45076

Müller,  Meinard
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Baak, A., Müller, M., & Seidel, H.-P. (2008). An Efficient Algorithm for Keyframe-based Motion Retrieval in the Presence of Temporal Deformations. In MM’08: Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium & Workshops (AREA’08, CommunicabilityMS’08, HCC’08, MIR’08, MS’08, SAME’08, SRMC’08, TVS’08, VNBA’08) (pp. 451-458). New York, NY: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1AE2-0
Zusammenfassung
In the last years, various algorithms have been proposed for automatic classification and retrieval of motion capture data. Here, one main difficulty is due to the fact that similar types of motions may exhibit significant spatial as well as temporal variations. To cope with such variations, previous algorithms often rely on warping and alignment techniques that are computationally time and cost intensive. In this paper, we present a novel keyframe-based algorithm that significantly speeds up the retrieval process and drastically reduces memory requirements. In contrast to previous index-based strategies, our recursive algorithm can cope with temporal variations. In particular, the degree of admissible deformation tolerance between the queried keyframes can be controlled by an explicit stiffness parameter. While our algorithm works for general multimedia data, we concentrate on demonstrating the practicability of our concept by means of the motion retrieval scenario. Our experiments show that one can typically cut down the search space from several hours to a couple of minutes of motion capture data within a fraction of a second.