FMDistance: A Fast and Effective Distance Function for Motion Capture Data
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Date
2008
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@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},
ISBN = {},
DOI = {10.2312/egs.20081027}
}