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Feature Driven Visualization of Video Content for Interactive Indexing

  • Jeroen Vendrig
  • Marcel Worring
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)

Abstract

When using visual video features in an interactive video in- dexing environment, it is necessary to visualize the meaning and impact of features to people that are not image processing experts, such as video librarians. An important method to visualize the relationship between the feature and the video is projection of feature values on the original video data.

In this paper, we describe the characteristics of video feature types with respect to visualization. In addition, requirements for the visualization of video features are distinguished. Several video visualization methods are evaluated against the requirements. Furthermore, for feature visualization we propose the backprojection method in combination with the evaluated video visualization methods.

We have developed the VidDex system which uses backprojection on various video visualization modes. By combining the visualization modes, the requirements for the feature characteristics identified can be met.

Keywords

Video Stream Video Content Video Shot Interactive Indexing Video Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Z. Aghbari, K. Kaneko, and A. Makinouchi. Vst-model: A uniform topological modeling of the visual-spatio-temporal video features. In Proc. of the 6th IEEE Int. Conf. on Multimedia Systems, volume 2, pages 163–168, 1999.Google Scholar
  2. 2.
    J.M. Boggs and D.W. Petrie. The art of watching films. Mayfield Publishing Company, Mountain View, CA, 5th edition, 2000.Google Scholar
  3. 3.
    C. Colombo, A. DelBimbo, and P. Pala. Semantics in visual information retrieval. IEEE Multimedia, 6(3):38–53, 1999.CrossRefGoogle Scholar
  4. 4.
    G. Davenport, T. Aguierre Smith, and N. Pincever. Cinematic principles for multimedia. IEEE Computer Graphics & Applications, pages 67–74, July 1991.Google Scholar
  5. 5.
    A. Hanjalic, R.L. Lagendijk, and J. Biemond. Automatically segmenting movies into logical story units, volume 1614 of Lecture Notes in Computer Science, pages 229–236. Springer-Verlag, Berlin, 1999.Google Scholar
  6. 6.
    H. Müller and E. Tan. Movie maps. In International Conference on Information Visualization, London, England, 1999. IEEE.Google Scholar
  7. 7.
    G.S. Pingali, Y. Jean, and I. Carlbom. LucentVision: A System for Enhanced Sports Viewing, volume 1614 of Lecture Notes in Computer Science, pages 689–696. Springer-Verlag, Berlin, 1999.Google Scholar
  8. 8.
    Y. Rui, T.S. Huang, and S. Mehrotra. Constructing table-of-content for videos. Multimedia Systems, Special section on Video Libraries, 7(5):359–368, 1999.Google Scholar
  9. 9.
    J.R. Smith and S.-F. Chang. Integrated spatial and feature image query. Multimedia Systems, 7(2):129–140, 1999.CrossRefGoogle Scholar
  10. 10.
    L. Teodosio and W. Bender. Salient video stills: Content and context preserved. In Proc. of the First ACM Int’l Conf. on Multimedia, pages 39–46, 1993.Google Scholar
  11. 11.
    K. Weixin, R. Yao, and L. Hanqing. A new scene breakpoint detection algorithm using slice of video stream. In H.H.S. Ip and A.W.M. Smeulders, editors, MI-NAR’98, pages 175–180, Hongkong, China, 1998. IAPR.Google Scholar
  12. 12.
    B.-L. Yeo and M.M. Yeung. Retrieving and visualizing video. Communications of the ACM, 40(12):43–52, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jeroen Vendrig
    • 1
  • Marcel Worring
    • 1
  1. 1.Intelligent Sensory Information Systems, Department of Computer ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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