Statistical Motion-Based Retrieval with Partial Query

  • Ronan Fablet
  • Patrick Bouthemy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


We present an original approach for motion-based retrieval involving partial query. More precisely, we propose an unified statistical framework both to extract entities of interest in video shots and to achieve the associated content-based characterization to be exploited for retrieval issues. These two stages rely on the characterization of scene activity in video sequences based on a non-parametric statistical modeling of motion information. Areas comprising relevant scene activity are extracted from an ascendant hierarchical classiffcation applied to the adjacency graph of an initial block-based partition of the image. Therefore, given a video base, we are able to construct a base of samples of entities of interest characterized by their associated scene activity model. The retrieval operations is then formulated as a Bayesian inference issue using the MAP criterion. We report different results of extraction of entities of interest in video sequences and examples of retrieval operations performed on a video base composed of a hundred samples.


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  1. 1.
    P. Aigrain, H-J. Zhang, and D. Petkovic. Content-based representation and retrieval of visual media: A state-of-the-art review. Multimedia Tools and Applications, 3(3):179–202, September 1996.CrossRefGoogle Scholar
  2. 2.
    P. Bouthemy, M. Gelgon, and F. Ganansia. A unified approach to shot change detection and camera motion characterization. IEEE Trans. on Circuits and Systems for Video Technology, 9(7):1030–1044, 1999.CrossRefGoogle Scholar
  3. 3.
    R. Brunelli, O. Mich, and C.M. Modena. A survey on the automatic indexing of video data. Jal of Vis. Comm. and Im. Repr., 10(2):78–112, 1999.CrossRefGoogle Scholar
  4. 4.
    S.-F. Chang, W. Chen, H.J. Meng, H. Sundaram, and D. Zhong. VideoQ-an Automatic content-based video search system using visual cues. In Proc. ACM Multimedia Conf., Seattle, November 1997.Google Scholar
  5. 5.
    R. Fablet and P. Bouthemy. Motion-based feature extraction and ascendant hierarchical classiffcation for video indexing and retrieval. In Proc. of 3rd Int. Conf. on Visual Information Systems, VISUAL’99, LNCS Vol 1614, pages 221–228, Amsterdam, June 1999. Springer.Google Scholar
  6. 6.
    R. Fablet, P. Bouthemy, and P. Pérez. Statistical motion-based video indexing and retrieval. In Proc. of 6th Int. Conf. on Content-Based Multimedia Information Access, RIAO’2000, pages 602–619, Paris, April 2000.Google Scholar
  7. 7.
    A.K. Jain, A. Vailaya, and W. Xiong. Query by video clip. Multimedia Systems, 7(5):369–384, 1999.CrossRefGoogle Scholar
  8. 8.
    A. Mitiche and P. Bouthemy. Computation and analysis of image motion: a synopsis of current problems and methods. Int. Journal of Computer Vision, 19(1):29–55, 1996.CrossRefGoogle Scholar
  9. 9.
    M.R. Naphade, T.T. Kristjansson, B.J. Frey, and T. Huang. Probabilistic multimedia objects (Multijects): a novel approach to video indexing and retrieval in multimedia systems. In Proc. of 5th IEEE Int. Conf. on Image Processing, ICIP’98, pages 536–5450, Chicago, October 1998.Google Scholar
  10. 10.
    C. Nastar, M. Mitschke, and C. Meilhac. Effcient query refinement for image retrieval. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’98, Santa Barbara, June 1998.Google Scholar
  11. 11.
    R. Nelson and R. Polana. Qualitative recognition of motion using temporal texture. Computer Vision, Graphics, and Image Processing, 56(1):78–99, July 1992.zbMATHGoogle Scholar
  12. 12.
    J.M. Odobez and P. Bouthemy. Robust multiresolution estimation of parametric motion models. Jal of Vis. Comm. and Im. Repr., 6(4):348–365, 1995.CrossRefGoogle Scholar
  13. 13.
    J.M. Odobez and P. Bouthemy. Separation of moving regions from background in an image sequence acquired with a mobile camera. In Video Data Compression for Multimedia Computing, chapter 8, pages 295–311. H. H. Li, S. Sun, and H. Derin,eds, Kluwer, 1997.Google Scholar
  14. 14.
    N. Vasconcelos and A. Lippman. A Bayesian framework for semantic content characterization. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’98, pages 566–571, Santa-Barbara, June 1998.Google Scholar
  15. 15.
    N. Vasconcelos and A. Lippman. A probabilistic architecture for content-based image retrieval. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’2000, Hilton Head, June 2000.Google Scholar
  16. 16.
    V. Vinod. Activity based video shot retrieval and ranking. In Proc. of 14th Int. Conf. on Pattern Recognition, ICPR’98, pages 682–684, Brisbane, August 1998.Google Scholar
  17. 17.
    H. Wactlar, T. Kanade, M. Smith, and S. Stevens. Intelligent access to digital video: The informedia project. IEEE Computer, 29(5):46–52, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ronan Fablet
    • 1
  • Patrick Bouthemy
    • 2
  1. 1.1IRISA / CNRSCampus universitaire de BeaulieuRennes CedexFrance
  2. 2.IRISA / INRIACampus universitaire de BeaulieuRennes CedexFrance

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