A human motion feature based on semi-supervised learning of GMM
Authors: Qi, T., Feng, Y., Xiao, J., Zhang, H., Zhuang, Y., Yang, X. and Zhang, J.
Journal: Multimedia Systems
Volume: 23
Issue: 1
Pages: 85-93
ISSN: 0942-4962
DOI: 10.1007/s00530-014-0429-2
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.
https://eprints.bournemouth.ac.uk/28146/
Source: Scopus
Preferred by: Xiaosong Yang
A human motion feature based on semi-supervised learning of GMM
Authors: Qi, T., Feng, Y., Xiao, J., Zhang, H., Zhuang, Y., Yang, X. and Zhang, J.
Journal: MULTIMEDIA SYSTEMS
Volume: 23
Issue: 1
Pages: 85-93
eISSN: 1432-1882
ISSN: 0942-4962
DOI: 10.1007/s00530-014-0429-2
https://eprints.bournemouth.ac.uk/28146/
Source: Web of Science (Lite)
A human motion feature based on semi-supervised learning of GMM
Authors: Qi, T., Feng, Y., Xiao, J., Zhang, H., Zhuang, Y., Yang, X. and Zhang, J.J.
Journal: Multimedia Systems
Volume: 23
Issue: 1
Pages: 85-93
ISSN: 0942-4962
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.
https://eprints.bournemouth.ac.uk/28146/
Source: BURO EPrints