Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Multimedia Systems
Published in: Multimedia Systems 1/2017

21-10-2014 | Special Issue Paper

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

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

Published in: Multimedia Systems | Issue 1/2017

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Robust visual tracking via discriminative appearance model based on sparse coding
next article Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Gleicher, M.: Motion editing with spacetime constraints. In: Proceedings of the 1997 symposium on interactive 3D graphics. pp. 139–148 (1997)
2.
Min, J., Liu, H., Chai, J.: Synthesis and editing of personalized stylistic human motion. In: Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games. pp. 39–46 (2010)
3.
Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. ACM Trans. Gr. (TOG) 21(3), 473–482 (2002)
4.
Jain, S., Ye, Y., Liu, C.: Optimization-based interactive motion synthesis. ACM Trans. Gr. (TOG) 28(1), 10 (2009)
5.
Heck, R., Gleicher, M.: Parametric motion graphs. In: Proceedings of the 2007 symposium on interactive 3D graphics and games, pp. 127–139 (2007)
6.
Hecker, C., Raabe, B., Enslow, R., DeWeese, J., Maynard, J., Prooijen, K.: Real-time motion retargeting to highly varied user-created morphologies. ACM Trans. Gr. (TOG) 27(3), 27 (2008)
7.
Yoshitaka, A., Ichikawa, T.: A survey on content-based retrieval for multimedia databases. IEEE Trans. Knowl. Data Eng. 11(1), 81–93 (1999)CrossRef
8.
Forbes, K., Fiume, E.: An efficient search algorithm for motion data using weighted PCA, In: Proceedings of ACM SIGGRAPH/Eurographics symposium on computer animation. pp. 67–76 (2005)
9.
Beaudoin, P., Poulin, P., van de Panne, M.: Adapting wavelet compression to human motion capture clips. In: Proceedings of graphics interface, pp. 313–318 (2007)
10.
Muller, M., Roder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Trans. Gr. (TOG) 24(3), 667–685 (2005)CrossRef
11.
Chen, C., Zhuang, Y., Xiao, J., Liang, Z.: Perceptual 3D pose distance estimation by boosting relational geometric features. Comput. Anim. Virtual Worlds 20(223), 267–277 (2009)CrossRef
12.
Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X., Zhang, J.: A semantic feature for human motion retrieval. Comput. Anim. Virtual Worlds 24(3–4), 399–407 (2013)CrossRef
13.
Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and viterbi path searching, IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1–8 (2007)
14.
Fernando, B., Fromont, E., Muselet, D., Sebban, M.: Supervised learning of Gaussian mixture models for visual vocabulary generation. Pattern Recognit. 45(2), 897–907 (2012)CrossRefMATH
15.
Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., Tian, Q.: Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc. 22(12), 5071–5084 (2013)MathSciNetCrossRef
16.
Xia, Y., Chen, J., Li, J., Zhang, Y.: Geometric discriminative features for aerial image retrieval in social media. Multimedia systems (2014)
17.
Zhang, L., Song, M., Zhao, Q., Liu, X., Bu, J., Chen, C.: Probabilistic graphlet transfer for photo cropping. IEEE Trans. Image Process. 22(2), 802–815 (2013)MathSciNetCrossRef
18.
Zhang, L., Yang, Y., Gao, Y., Yu, Y., Wang, C., Li, X.: A probabilistic associative model for segmenting weakly-supervised images. IEEE Trans. Image Process. 23(9), 4150–4159 (2014)MathSciNetCrossRef
19.
Zhang, L., Gao, Y., Lu, K., Shen, J., Ji, R.: Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans. Multimed. 16(2), 470–479 (2014)CrossRef
20.
Sun, F., Xu, M., Li, H., Hao, S.: Social video annotation by combining features with a tri-adaptation approach. Multimedia systems (2014)
21.
Liu, G., Zhang, J., Wang, W., McMillan, L.: A system for analyzing and indexing human-motion databases. In: Proceedings of ACM SIGMOD international conference on management of data, pp. 924–926 (2005)
22.
Safonova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Gr. (TOG) 23(3), 514–521 (2004)CrossRef
23.
Liu, G., McMillan, L.: Segment-based human motion compression, In: Proceedings of ACM SIGGRAPH/Eurographics symposium on computer animation, pp. 127–135 (2006)
24.
Gu, Q., Peng, J., Deng, Z.: Compression of human motion capture data using motion pattern indexing. Compu. Gr. Forum 28(1), 1–12 (2009)CrossRef
25.
Deng, Z., Gu, Q., Li, Q.: Perceptually consistent example-based human motion retrieval, In: Proceedings of the 2009 symposium on interactive 3D graphics and games, pp. 191–198 (2009)
26.
Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Gr. (TOG) 23(3), 559–568 (2004)CrossRef
27.
Tang, J.K., Leung, H., Komura, T., Shum, H.P.: Emulating human perception of motion similarity. Comput. Anim. Virtual Worlds 19(3–4), 211–221 (2008)CrossRef
28.
Tang, J.K., Leung, H.: Retrieval of logically relevant 3D human motions by adaptive feature selection with graded relevance feedback. Pattern Recognit. Lett. 33(4), 420–430 (2012)CrossRef
29.
Chen, S., Sun, Z., Li, Y., Li, Q.: Partial similarity human motion retrieval based on relative geometry features, In fourth international conference on digital home (ICDH), pp. 298–303 (2012)
30.
Yu, T., Shen, X., Li, Q., Geng, W.: Motion retrieval based on movement notation language. Comput. Anim. Virtual Worlds 16(3–4), 273–282 (2005)CrossRef
31.
Qi, T., Xiao, J., Zhuang, Y., Zhang, H., Yang, X., Zhang, J., Feng, Y.: Real-time motion data annotation via action string. Comput. Anim. Virtual Worlds 25(3–4), 293–302 (2014)
32.
Han, Y., Yang, Y., Ma, Z., Shen, H., Sebe, N., Zhou, X.: Image attribute adaptation. IEEE Trans. Multimed. 16(4), 1115–1126 (2014)CrossRef
33.
Han Y., Yang Y., Yan Y., Ma Z., Sebe N., Zhou X.: Semi-supervised feature selection via spline regression for video semantic recognition. IEEE Trans. Neural Netw. Learn. Syst. (2014)
34.
Müller, M., Röder, T., Clausen, M., Eberhardt B., Krüger, B., Weber, A.: Documentation mocap database HDM05, Technical Reports CG-2007-2, Universität Bonn (2007)
35.
Yang, Y., Ma, Z., Hauptmann, G., Sebe, N.: Feature selection for multimedia analysis by sharing information among multiple tasks. IEEE Trans. Multim. 15(3), 661–669 (2013)CrossRef
Metadata
Title
A human motion feature based on semi-supervised learning of GMM
Authors
Tian Qi
Yinfu Feng
Jun Xiao
Hanzhi Zhang
Yueting Zhuang
Xiaosong Yang
Jianjun Zhang
Publication date
21-10-2014
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 1/2017
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-014-0429-2

Other articles of this Issue 1/2017

Multimedia Systems 1/2017 Go to the issue

Special Issue Paper

Image feature detection algorithm based on the spread of Hessian source

Special Issue Paper

Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

Special Issue Paper

Graph-based clustering and ranking for diversified image search

Special Issue Paper

Tag relevance fusion for social image retrieval

Special Issue Paper

Semi-supervised tensor learning for image classification

Special Issue Paper

Learning for classification of traffic-related object on RGB-D data