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A Spatio-Temporal and a Probabilistic Approach for Video Retrieval

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Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

In this chapter we address two approaches to extract high-level concepts from video footage and show the integrated use of both. We also describe an experiment used for validation.

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References

  1. J. F. Allen. Maintaining Knowledge about Temporal Intervals. Communications of ACM, 26(11):832–843, 1983.

    Article  MATH  Google Scholar 

  2. L. Baum, T. Petrie, G. Soules, and N. Weiss. A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. Annals of Mathematical Statistics, 41(1):164–171.

    Google Scholar 

  3. M. Egenhofer and R. Franzosa. Point-set topological spatial relations. International Journal of Geographic Information Systems, 5(2):161–174.

    Google Scholar 

  4. A.M. Ferman and A.M. Tekalp. Probabilistic Analysis and Extraction of Video Content. In Procceeding of IEEE ICIP, volume 2, pages 536–540, Tokyo, Japan, 1998.

    Google Scholar 

  5. Y. Gong, L.T. Sin, C.H. Chuan, H-J. Zhang, and M. Sakauchi. Automatic Parsing of TV Soccer Programs. In Procceeding of IEEE International Conference on Multimedia Computing and Systems, pages 167–174, Washington D.C., 1995.

    Google Scholar 

  6. N. Haering, R.J. Qian, and M.I. Sezan. A Semantic Event-Detection Approach and its Application to Detecting Hunts in Wildlife Video. IEEE Transactions on Circuits and Systems for Video Technology, 10(6):857–868, 2000.

    Article  Google Scholar 

  7. S. Intille and A. Bobick. Visual Tracking Using Closed-Worlds. Technical Report 294, MIT Media Laboratory, 1994.

    Google Scholar 

  8. T-L. Liu and D. Geiger. Approximate Tree Matching and Shape Similarity. In Proceedings of the 7th Intl. Conference on Compute Vision, pages 456–462, Greece, 1999.

    Google Scholar 

  9. M. Naphade, T. Kristjansson, B. Frey, and T.S. Huang. Probabilistic Multimedia Objects (Multijects): A Novel Approach to Indexing and Retrieval in Multimedia Systames. In Procceeding of IEEE ICIP, volume 3, pages 536–540, Chicago, IL, 1998.

    Google Scholar 

  10. M. Petković. Content-Based Video Retrieval Supported by Database Technology. PhD thesis, Centre for Telematich and Information Technology, Enschede, The Netherlands, 2003.

    Google Scholar 

  11. M. Petković and W. Jonker. Content-Based Video Retrieval by Integrating Spatio Temporal and Stochastic Recognition of Events. In Procceeding of IEEE Intl. Workshop on Detection and Recognition of Events in Video, pages 75–82, Vancouver, Canada, 2001.

    Google Scholar 

  12. M. Petkovic, M. A. Windhouwer, R. van Zwol, H. E. Blok, P. M. G. Apers, M. L. Kersten, and W. Jonker. Content-based video indexing for the support of digital library search. In Proceedings of the 18th International Conference on Data Engineering (ICDE 2002), San Jose, CA, USA, pages 494–495, Washington, DC, USA, 2002. IEEE Computer Society. http://doi.ieeecomputersociety.org/10.1109/ICDE.2002.994766, issn 1063-6382.

    Google Scholar 

  13. G. Pingali, Y. Jean, A. Opalach, and I. Carlbom. LucentVision: Converting Real World Events into Multimedia Expiriences. In Procceeding of IEEE Intl. Conference on Multimedia and Expo (ICME), volume 3, pages 1433–1436, New York, 2000.

    Google Scholar 

  14. R. Rosales and A. Sclaroff. Specialized Mappings and the Estimation of Human Body Pose from a Single Image. In Workshop on Human Motion (HUMO). IEEE.

    Google Scholar 

  15. Y. Rui, A. Gupta, and A. Acero. Automatically Extracting Highlights for TV Baseball Programs. In Procceeding of ACM Multimedia, pages 105–115, Los Angeles, CA, 2000.

    Google Scholar 

  16. M. Shah and R. Jain. Motion-Based Recognition. Kluwer Academic Publisher, 1997.

    Google Scholar 

  17. G. Sudhir, J. Lee, and A. Jain. Automatic Classification of Tennis Video for High-level Content-based Retrieval. In Proceedings of IEEE Intl. Workshop on Content-based Access and Image and Video Databases, pages 81–90, Bombay, India, 1998.

    Google Scholar 

  18. N. Vasconcelos and A. Lippman. Bayesian Modeling of Video Editing and Structure: Semantic Features for Video Sumarization and Browsing. In Procceeding of IEEE ICIP, volume 2, pages 550–555, Chicago, IL, 1998.

    Google Scholar 

  19. Z. Živković, F. van der Heijden, M. Petković, and W. Jonker. Image Processing and Feature Extraction for Recognizing Strokes in Tennis Game Videos. In Proceedings of 7th Annual Conference of the Advances School for Computing and Imaging, pages 262–266, The Netherlands, 2001.

    Google Scholar 

  20. J. Yandell. Visual Tennis. Human Kinetics, 1999.

    Google Scholar 

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Petković, M., Jonker, W., Blanken, H. (2007). A Spatio-Temporal and a Probabilistic Approach for Video Retrieval. In: Blanken, H.M., Blok, H.E., Feng, L., de Vries, A.P. (eds) Multimedia Retrieval. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72895-5_9

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  • DOI: https://doi.org/10.1007/978-3-540-72895-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72894-8

  • Online ISBN: 978-3-540-72895-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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