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    FMDistance: A Fast and Effective Distance Function for Motion Capture Data

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    Date
    2008
    Author
    Onuma, Kensuke
    Faloutsos, Christos
    Hodgins, Jessica K. ORCID
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    Abstract
    Given several motion capture sequences, of similar (but not identical) length, what is a good distance function? We want to find similar sequences, to spot outliers, to create clusters, and to visualize the (large) set of motion capture sequences at our disposal. We propose a set of new features for motion capture sequences. We experiment with numerous variations (112 feature-sets in total, using variations of weights, logarithms, dimensionality reduction), and we show that the appropriate combination leads to near-perfect classification on a database of 226 actions with twelve different categories, and it enables visualization of the whole database as well as outlier detection.
    BibTeX
    @inproceedings {10.2312:egs.20081027,
    booktitle = {Eurographics 2008 - Short Papers},
    editor = {Katerina Mania and Eric Reinhard},
    title = {{FMDistance: A Fast and Effective Distance Function for Motion Capture Data}},
    author = {Onuma, Kensuke and Faloutsos, Christos and Hodgins, Jessica K.},
    year = {2008},
    publisher = {The Eurographics Association},
    DOI = {10.2312/egs.20081027}
    }
    URI
    http://dx.doi.org/10.2312/egs.20081027
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    Theme by @mire NV
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