Advertisement

Cobra: A Prototype of a Video DBMS

  • Milan Petković
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (MMSA, volume 25)

Abstract

An increasing number of large video libraries, which are becoming publicly available, result in a demand for techniques that can manipulate the video data based on content. This has led to a wide range of research on techniques for video database management systems (see [1–4] for reviews). However, a traditional database management system does not provide enough facilities for managing and retrieving video contents. Therefore, in the preceding chapters, we have recommended some solutions for the major problems. First, we have proposed a suitable video framework and then several knowledge-based methods for interpreting raw data into semantic content.

Keywords

Hide Markov Model Video Sequence Digital Library Video Processing Dynamic Bayesian Network 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    W. Grosky, “Managing Multimedia Information in Database Systems”, Communications of the ACM, 40 (12), 1997, pp. 73–80.CrossRefGoogle Scholar
  2. [2]
    A. Yoshitaka, T. Ichikawa, “A Survey on Content-Based Retrieval for Multimedia Databases”, IEEE Transactions on Knowledge and Data Engineering, 11(1), 1999, pp. 81–93.Google Scholar
  3. [3]
    A. Del Bimbo, Visual Information Retrieval, Morgan Kaufmann, San Francisco, California, 1999.Google Scholar
  4. [4]
    W. Al-Khatib, Y. Day, A. Ghafoor, P. Berra, “Semantic Modeling and Knowledge Representation in Multimedia Databases”, IEEE Transactions on Knowledge and Data Engineering, 11(1), 1999, pp. 64–80.Google Scholar
  5. [5]
    M. Petkovic, W. Jonker, V. Mihajlovic, “Extending a DBMS to Support Content-Based Video Retrieval: A Formula 1 Case Study”, XML-Based Data Management and Multimedia Engineering — EDBT, A.B. Chaudhri, R. Unland, C. Djeraba, W. Lindner (Eds.), 2002 LNCS 2490, Springer Verlag, 2002, pp. 318–341.Google Scholar
  6. [6]
    P. Bonz, A.N. Wilschut, M.L. Kersten, “Flattering an objects algebra to provide performance”, In Proc. of the IEEE ICDE, Orlando, 1998, pp. 568–577.Google Scholar
  7. [7]
    P. Boncz, M.L. Kersten, Monet: “An Impressionist Sketch of an Advanced Database System”, Basque International Workshop on Information Technology, San Sebastian, 1995.Google Scholar
  8. [8]
    A. R. Schmidt, M. A. Windhouwer, and M. L. Kersten, “Feature Grammars”, In Proceedings of the International Conference on Systems Analysis and Synthesis, Orlando, FL, USA, 1999.Google Scholar
  9. [9]
    M. A. Windhouwer, A. R. Schmidt, M. L. Kersten, “Acoi: A System for Indexing Multimedia objects”, In Proceedings of International workshop on Information Integration and Web-based Applications and Services, Indonesia, November 1999.Google Scholar
  10. [10]
    P. Linz, An Introduction to Formal Languages and Automata, second edition, Jones and Barlett Publications, 1997.Google Scholar
  11. [11]
    M. Petkovic, R. Zwol, H. E. Blok, W. Jonker, P. M. G. Apers, M. Windhouwer, M. Kersten, “Content-based Video Indexing for the Support of Digital Library Search”, In Proc. of the 18` f ’ IEEE International Conference on Data Engineering (ICDE), San Jose, USA, February 2002.Google Scholar
  12. [12]
    L. R. Rabiner, B.H. Juang, “An Introduction to Hidden Markov Models”, IEEE ASSP Magazine, January 1986.Google Scholar
  13. [13]
    M.Petkovic, W. Jonker, “Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events”, In the Proceedings of the IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, 2001, pp. 75–82.Google Scholar
  14. [14]
    Tennis Australia, Australian Open tennis tournament — grand slam tennis — official site by IBM, http://www.ausopen.org, January 2001.Google Scholar
  15. [15]
    H. E. Blok, M. Windhouwer, R. Zwol, M. Petkovic, P. M. G. Apers, M. Kersten, W. Jonker, “Flexible and Scalable Digital Library Search”, In Proc. of the 27th International Conference on Very Large Databases, Roma, Italy, September 2001, pp. 705–706.Google Scholar
  16. [16]
    Advanced Multimedia Indexing and Searching (AMIS), The Netherlands Foundation for Scientific Research grant no. 612–21–201.Google Scholar
  17. [17]
    P. G. M. Apers, M. L. Kersten, DMW: Digital Media Warehouse Systems, Technical Report, Telemetics Institute, Enschede, the Netherlands, June 1998.Google Scholar
  18. [18]
    M. A. Windhouwer, R. van Zwol, A. R. Schmidt, M. Petkovic, and H. E. Blok, “Flexible and scalable digital library search”, In Web-enabled Systems Integration: Challenges and Google Scholar
  19. Practices,(Ajantha Dahanayake and Waltraud Gerhardt,eds.), Idea Group Publishing, Hershey, Pennsylvania, USA, 2002.Google Scholar
  20. [19]
    H.E. Blok, Database Optimization Aspects for Information Retrieval, Ph.D. thesis, Twente University Press, April 2002.Google Scholar
  21. [20]
    R. van Zwol, P. G. M. Apers, “The Webspace Method: On the Integration of the Database Technology with Information Retrieval” In Proceedings of the Conference of Information and Knowledge Management (CIKM’00), Washington D.C., 2000.Google Scholar
  22. [21]
    Roelof van Zwol, Modelling and searching web-based document collections, Ph.D. Thesis, Enschede, The Netherlands, 2002.Google Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Milan Petković

There are no affiliations available

Personalised recommendations

Cite chapter

Buy options