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Efficient motion data indexing and retrieval with local similarity measure of motion strings

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Abstract

Widely used in data-driven computer animation, motion capture data exhibits its complexity both spatially and temporally. The indexing and retrieval of motion data is a hard task that is not totally solved. In this paper, we present an efficient motion data indexing and retrieval method based on self-organizing map and Smith–Waterman string similarity metric. Existing motion clips are first used to train a self-organizing map and then indexed by the nodes of the map to get the motion strings. The Smith–Waterman algorithm, a local similarity measure method for string comparison, is used in clustering the motion strings. Then the motion motif of each cluster is extracted for the retrieval of example-based query. As an unsupervised learning approach, our method can cluster motion clips automatically without needing to know their motion types. Experiment results on a dataset of various kinds of motion show that the proposed method not only clusters the motion data accurately but also retrieves appropriate motion data efficiently.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shuangyuan Wu, Shihong Xia, Zhaoqi Wang & Chunpeng Li

  2. Graduate School of the Chinese Academy of Sciences, Beijing, China

    Shuangyuan Wu

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  1. Shuangyuan Wu
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  2. Shihong Xia
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  3. Zhaoqi Wang
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  4. Chunpeng Li
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Correspondence to Shuangyuan Wu.

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Wu, S., Xia, S., Wang, Z. et al. Efficient motion data indexing and retrieval with local similarity measure of motion strings. Vis Comput 25, 499–508 (2009). https://doi.org/10.1007/s00371-009-0345-1

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  • DOI: https://doi.org/10.1007/s00371-009-0345-1

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