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Multimedia Systems
Erschienen in: Multimedia Systems 1/2017

21.10.2014 | Special Issue Paper

A human motion feature based on semi-supervised learning of GMM

verfasst von: Tian Qi, Yinfu Feng, Jun Xiao, Hanzhi Zhang, Yueting Zhuang, Xiaosong Yang, Jianjun Zhang

Erschienen in: Multimedia Systems | Ausgabe 1/2017

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Abstract

Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation.
Vorheriger Artikel Robust visual tracking via discriminative appearance model based on sparse coding
Nächster Artikel Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

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Metadaten
Titel
A human motion feature based on semi-supervised learning of GMM
verfasst von
Tian Qi
Yinfu Feng
Jun Xiao
Hanzhi Zhang
Yueting Zhuang
Xiaosong Yang
Jianjun Zhang
Publikationsdatum
21.10.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 1/2017
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-014-0429-2

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