[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

An efficient retrieval mechanism is essential to search for a particular motion from a large corpus. This has proven to be a challenging task as human motion is high dimensional in both spatial and temporal domains. Besides, semantically similar motions are not necessary numerically similar because of the speed variations. In this paper, we propose a temporal sparse representation (TSR) for human motion retrieval. Compared with existing methods that adopt sparse representation, our TSR encodes the temporal information within motions and thus generates a more compact and discriminative representation. In addition, we propose a spatial temporal pyramid matching kernel based on TSR, which can be used for logical comparison between motions. Moreover, it improves the effectiveness of motion retrieval in terms of accuracy and speed. Through our experimental evaluations, we demonstrate that the proposed human motion retrieval system has better performance and allows the user to retrieve desired motions from the motion capture database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  2. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 33–61 (1998)

    Article  MathSciNet  Google Scholar 

  3. Corporation, M.: Kinect for windows sdk beta programming guide version 1.7. (2013)

  4. Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997)

    MATH  MathSciNet  Google Scholar 

  5. 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, I3D ’09, pp. 191–198 (2009)

  6. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  7. Huang, T., Liu, H., Ding, G.: Motion retrieval based on kinetic features in large motion database. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, ICMI ’12, pp. 209–216 (2012)

  8. Jin, Y., Prabhakaran, B.: Knowledge discovery from 3d human motion streams through semantic dimensional reduction. ACM Trans. Multimedia Comput. Commun. Appl. 7(2), 9:1–9:20 (2011)

  9. Kapadia, M., Chiang, I.K., Thomas, T., Badler, N.I., Kider Jr., J.T.: Efficient motion retrieval in large motion databases. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D ’13, pp. 19–28 (2013)

  10. Komura, T., Ho, E.S.L., Lau, R.W.H.: Animating reactive motion using momentum-based inverse kinematics. Comput. Animat. Virtual Worlds 16(3–4), 213–223 (2005)

    Article  Google Scholar 

  11. Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph. 23(3), 559–568 (2004)

    Article  Google Scholar 

  12. Lai, R.Y.Q., Yuen, P.C., Lee, K.W., Lai, J.H.: Interactive character posing by sparse coding. CoRR abs/1201.1409 (2012)

  13. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)

  14. Lu, Z., Ip, H.H.: Spatial markov kernels for image categorization and annotation. Trans. Sys. Man Cyber. Part B 41(4), 976–989 (2011)

    Article  Google Scholar 

  15. Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  16. Mou, L., Huang, T., Tian, Y., Jiang, M., Gao, W.: Content-based copy detection through multimodal feature representation and temporal pyramid matching. ACM Trans. Multimedia Comput. Commun. Appl. 10(1), 5:1–5:20 (2013)

  17. Müller, M., Röder, T.: Motion templates for automatic classification and retrieval of motion capture data. In: Proceedings of the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA), pp. 137–146. Vienna, Austria (2006)

  18. Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation mocap database hdm05. Tech. Rep. CG-2007-2, Universität Bonn (2007)

  19. Pati, Y., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, 1993, vol. 1, pp. 40–44 (1993)

  20. Pradhan, G., Prabhakaran, B.: Indexing 3-d human motion repositories for content-based retrieval. IEEE Trans. Inf. Technol. Biomed. 13(5), 802–809 (2009)

    Article  Google Scholar 

  21. Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X., Zhang, J.: A semantic feature for human motion retrieval. Comput. Animat. Virtual Worlds 24(3–4), 399–407 (2013)

    Article  Google Scholar 

  22. Shum, H., Ho, E.S.: Real-time physical modelling of character movements with microsoft kinect. In: Proceedings of the 18th ACM Symposium on Virtual Reality Software and Technology, VRST ’12, pp. 17–24 (2012)

  23. Sun, C., Junejo, I., Foroosh, H.: Motion retrieval using low-rank subspace decomposition of motion volume. Comput. Graph. Forum, vol. 30, no. 7 (2011)

  24. Tang, J.K.T., Leung, H.: Retrieval of logically relevant 3d human motions by adaptive feature selection with graded relevance feedback. Pattern Recogn. Lett. 33(4), 420–430 (2012)

    Article  Google Scholar 

  25. Ward, R.K., Guha, T.: Learning sparse representations for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1576–1588 (2012)

    Article  Google Scholar 

  26. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  27. Wu, S., Xia, S., Wang, Z., Li, C.: Efficient motion data indexing and retrieval with local similarity measure of motion strings. Vis. Comput. 25(5–7), 499–508 (2009)

    Article  Google Scholar 

  28. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR (2009)

  29. Zhu, M., Sun, H., Deng, Z.: Quaternion space sparse decomposition for motion compression and retrieval. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’12, pp. 183–192 (2012)

Download references

Acknowledgments

The work described in this paper was supported by a grant from City University of Hong Kong (Project No. 7004045), National Natural Science Foundation of China under Grant 61202231, Beijing Natural Science Foundation of China under Grant 4132037, and Ph.D. Programs Foundation of Ministry of Education of China under Grant 20120001120130.

Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Liuyang Zhou & Howard Leung

  2. Key Laboratory of Data Engineering and Knowledge Engineering (MOE), School of Information, Renmin University of China, Beijing , 100872, China

    Zhiwu Lu

  3. Noah’s Ark Lab, Huawei, Shatin, Hong Kong

    Lifeng Shang

Authors
  1. Liuyang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiwu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howard Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lifeng Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Howard Leung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, L., Lu, Z., Leung, H. et al. Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval. Vis Comput 30, 845–854 (2014). https://doi.org/10.1007/s00371-014-0957-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-014-0957-y

Keywords

Search

Navigation