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Motion Segmentation and Retrieval for 3D Video Based on Modified Shape Distribution


A similar motion search and retrieval system for 3D video are presented based on a modified shape distribution algorithm. 3D video is a sequence of 3D models made for a real-world object. In the present work, three fundamental functions for efficient retrieval have been developed: feature extraction, motion segmentation, and similarity evaluation. Stable-shape feature representation of 3D models has been realized by a modified shape distribution algorithm. Motion segmentation has been conducted by analyzing the degree of motion using the extracted feature vectors. Then, similar motion retrieval has been achieved employing the dynamic programming algorithm in the feature vector space. The experimental results using 3D video sequences of dances have demonstrated very promising results for motion segmentation and retrieval.


  1. 1.

    Kanade T, Rander P, Narayanan PJ: Virtualized reality: constructing virtual worlds from real scenes. IEEE Multimedia 1997,4(1):34–47. 10.1109/93.580394

    Article  Google Scholar 

  2. 2.

    Wurmlin S, Lamboray E, Staadt OG, Gross MH: 3D video recorder. Proceedings of the 10th Pacific Conference on Computer Graphics and Applications, October 2002, Beijing, China 325–334.

    Google Scholar 

  3. 3.

    Matsuyama T, Wu X, Takai T, Wada T: Real-time dynamic 3-D object shape reconstruction and high-fidelity texture mapping for 3-D video. IEEE Transactions on Circuits and Systems for Video Technology 2004,14(3):357–369. 10.1109/TCSVT.2004.823396

    Article  Google Scholar 

  4. 4.

    Tomiyama K, Orihara Y, Katayama M, Iwadate Y: Algorithm for dynamic 3D object generation from multi-viewpoint images. Three-Dimensional TV, Video, and Display III, October 2004, Philadelphia, Pa, USA, Proceedings of SPIE 5599: 153–161.

    Article  Google Scholar 

  5. 5.

    Ito Y, Saito H: Free-viewpoint image synthesis from multiple-view images taken with uncalibrated moving cameras. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 3: 29–32.

    Google Scholar 

  6. 6.

    Habe H, Katsura Y, Matsuyama T: Skin-off: representation and compression scheme for 3D video. Proceedings of Picture Coding Symposium (PCS '04), December 2004, San Francisco, Calif, USA 301–306.

    Google Scholar 

  7. 7.

    Müller K, Smolic A, Kautzner M, Eisert P, Wiegand T: Predictive compression of dynamic 3D meshes. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 1: 621–624.

    Google Scholar 

  8. 8.

    Shiratori T, Nakazawa A, Ikeuchi K: Rhythmic motion analysis using motion capture and musical information. Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI '03), July–August 2003, Tokyo, Japan 89–92.

    Google Scholar 

  9. 9.

    Kahol K, Tripathi P, Panchanathan S: Automated gesture segmentation from dance sequences. Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, May 2004, Seoul, Korea 883–888.

    Google Scholar 

  10. 10.

    Barbič J, Safonova A, Pan J-Y, Faloutsos C, Hodgins JK, Pollard NS: Segmenting motion capture data into distinct behaviors. Proceedings of Graphics Interface (GI '04), May 2004, London, UK 185–194.

    Google Scholar 

  11. 11.

    Lu C, Ferrier NJ: Repetitive motion analysis: segmentation and event classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(2):258–263. 10.1109/TPAMI.2004.1262196

    Article  Google Scholar 

  12. 12.

    Takano W, Nakamura Y: Segmentation of human behavior patterns based on the probabilistic correlation. Proceedings of the 19th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI '05), June 2005, Kitakyushu, Japan 3F1-01

    Google Scholar 

  13. 13.

    Sakamoto Y, Kuriyama S, Kaneko T: Motion map: image-based retrieval and segmentation of motion data. Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, August 2004, Grenoble, France 259–266.

    Google Scholar 

  14. 14.

    Chiu C-Y, Chao S-P, Wu M-Y, Yang S-N, Lin H-C: Content-based retrieval for human motion data. Journal of Visual Communication and Image Representation 2004,15(3):446–466. 10.1016/j.jvcir.2004.04.004

    Article  Google Scholar 

  15. 15.

    Müller M, Röder T, Clausen M: Efficient content-based retrieval of motion capture data. Proceedings of ACM SIGGRAPH, 2005, Vienna, Austria 677–685.

    Google Scholar 

  16. 16.

    Xu J, Yamasaki T, Aizawa K: 3D video segmentation using point distance histograms. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 1: 701–704.

    Google Scholar 

  17. 17.

    Xu J, Yamasaki T, Aizawa K: Effective 3D video segmentation based on feature vectors using spherical coordinate system. Meeting on Image Recognition and Understanding (MIRU '05), July 2005, Hyogo, Japan 136–143.

    Google Scholar 

  18. 18.

    Yamasaki T, Aizawa K: Motion segmentation of 3D video using modified shape distribution. Proceedings of IEEE International Conference on Multimedia & Expo (ICME '06), July 2006, Toronto, Ontario, Canada

    Google Scholar 

  19. 19.

    Yamasaki T, Aizawa K: Similar motion retrieval of 3D video based on modified shape distribution. Proceedings of IEEE International Conference on Image Processing (ICIP '06), October 2006, Atlanta, Ga, USA

    Google Scholar 

  20. 20.

    Bellman R, Dreyfus S: Applied Dynamic Programming. Princeton University Press, Princeton, NJ, USA; 1962.

    Book  Google Scholar 

  21. 21.

    Bertsekas DP: Dynamic Programming and Optimal Control (Volume One). Athena Scientific, Belmont, Mass, USA; 1995.

    Google Scholar 

  22. 22.

    Matsuyama T, Wu X, Takai T, Nobuhara S: Real-time 3D shape reconstruction, dynamic 3D mesh deformation, and high fidelity visualization for 3D video. Computer Vision and Image Understanding 2004,96(3):393–434. 10.1016/j.cviu.2004.03.012

    Article  Google Scholar 

  23. 23.

    Tangelder JWH, Veltkamp RC: A survey of content based 3D shape retrieval methods. Proceedings of International Conference on Shape Modeling and Applications (SMI '04), June 2004, Genova, Italy 145–156.

    Google Scholar 

  24. 24.

    Osada R, Funkhouser T, Chazelle B, Dobkin D: Shape distributions. ACM Transactions on Graphics 2002,21(4):807–832. 10.1145/571647.571648

    MathSciNet  Article  Google Scholar 

  25. 25.

    Ney HJ, Ortmanns S: Dynamic programming search for continuous speech recognition. IEEE Signal Processing Magazine 1999,16(5):64–83. 10.1109/79.790984

    Article  Google Scholar 

  26. 26.

    Ney H, Ortmanns S: Progress in dynamic programming search for LVCSR. Proceedings of the IEEE 2000,88(8):1224–1240. 10.1109/5.880081

    Article  Google Scholar 

  27. 27.

    Amini AA, Weymouth TE, Jain RC: Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990,12(9):855–867. 10.1109/34.57681

    Article  Google Scholar 

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Correspondence to Toshihiko Yamasaki.

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Yamasaki, T., Aizawa, K. Motion Segmentation and Retrieval for 3D Video Based on Modified Shape Distribution. EURASIP J. Adv. Signal Process. 2007, 059535 (2006).

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  • Feature Vector
  • Feature Extraction
  • Dynamic Programming
  • Video Sequence
  • Retrieval System