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

This source preferred by Xiaosong Yang

This data was imported from Scopus:

Authors: Qi, T., Feng, Y., Xiao, J., Zhang, H., Zhuang, Y., Yang, X. and Zhang, J.

http://eprints.bournemouth.ac.uk/28146/

Journal: Multimedia Systems

Volume: 23

Issue: 1

Pages: 85-93

ISSN: 0942-4962

DOI: 10.1007/s00530-014-0429-2

© 2014, Springer-Verlag Berlin Heidelberg. 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.

This data was imported from Web of Science (Lite):

Authors: Qi, T., Feng, Y., Xiao, J., Zhang, H., Zhuang, Y., Yang, X. and Zhang, J.

http://eprints.bournemouth.ac.uk/28146/

Journal: MULTIMEDIA SYSTEMS

Volume: 23

Issue: 1

Pages: 85-93

eISSN: 1432-1882

ISSN: 0942-4962

DOI: 10.1007/s00530-014-0429-2

The data on this page was last updated at 05:24 on October 24, 2020.