Semantic Reasoning based Video Database Systems

  • Duc A. Tran
  • Kien A. Hua
  • Khanh Vu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1873)


A constraint of existing content-based video data models is that each modeled semantic description must be associated with time intervals exactly within which it happens and semantics not related to any time interval are not considered. Consequently, users are provided with limited query capabilities. This paper is aimed at developing a novel model with two innovations: (1) Semantic contents not having related time information can be modeled as ones that do; (2) Not only the temporal feature of semantic descriptions, but also the temporal relationships among themselves are components of the model. The query system is by means of reasoning on those relationships.

To support users’ access, a video algebra and a video calculus as formal query languages, which are based on semantic relationship reasoning, are also presented.


Free Variable Atomic Formula Semantic Description Video Object Video Retrieval 
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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  1. 1.
    S. Adali, K. S. Candan, S.-S. Chen, K. Erol, and V. S. Subrahmanian. Advanced video information system: Data structures and query processing. AGM-Springer Multimedia Systems Journal, 1996.Google Scholar
  2. 2.
    J. F. Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, Springer-Verlag, 26(11), 1983.Google Scholar
  3. 3.
    E. Ardizzone and M. L. Cascia. Automatic video database indexing and retrieval. Multimedia Tools and Applications, 4(1), 1997.Google Scholar
  4. 4.
    S.-F. Chang, W. Chen, H. Meng, H. Sundaram, and D. Zhong. Videoq: An automatedn content based video search system using visual cues. In ACM Multimedia, November 1997.Google Scholar
  5. 5.
    T.-S. Chua and L.-Q. Ruan. A video retrieval and sequencing system. ACM Transactions on Information Systems, 13(4), 1995.Google Scholar
  6. 6.
    Y. Day, S. Dagtag, M. Iino, A. Khoakhar, and A. Ghafoor. A multi-level abstraction and modeling in video databases. ACM Springer-Verlag Multimedia Systems, 7(5), 1999.Google Scholar
  7. 7.
    C. Decleir and M.-S. Hacid. A database approach for modeling and querying video data. In IEEE Data Engineering, Australia, 1999.Google Scholar
  8. 8.
    A. Hampapur, R. Jain, and T. Weymouth. Production model based digital video segmentation. Journal of Multimedia Tools and Applications, 1(1), 1995.Google Scholar
  9. 9.
    H. Jiang, D. Montesi, and K. Elmagarmid. Videotext database systems. In IEEE Int’l Conf. on Multimedia Computing and Systems, Ontario, Canada, June 1997.Google Scholar
  10. 10.
    J. Oh and K. A. Hua. An efficient and cost-effective technique for browsing, querying and indexing large video databases. In ACM SIGMOD, Dallas, TX, May 2000.Google Scholar
  11. 11.
    J. Oh, K. A. Hua, and N. Liang. A content-based scene change detection and classification technique using background tracking. In SPIE Conf. on Multimedia Computing and Networking, San Jose, CA, January 2000.Google Scholar
  12. 12.
    E. Oomoto and K. Tanaka. Ovid: Design and implementation of a video-object database system. IEEE Trans. on Knowledge and Data Engineering, 5, August 1993.Google Scholar
  13. 13.
    Y. Rui, S. Huang, and S. Mehrotra. Constructing table-of-content for videos. ACM Springer-Verlag Multimedia Systems, 7(5), 1999.Google Scholar
  14. 14.
    T. G. A. Smith and G. Davenport. The stratification system: A design environment for random access video. In Proceedings of the 3rd Int’l Workshop on Network and Operating System Support for Digital Audio and Video, La Jolla, CA, 1992.Google Scholar
  15. 15.
    D. Swanberg, C.-F. Shu, and R. Jain. Knowledge guided parsing in video databases. In SPIE Conf. on Image and Video Processing, volume 1908, San Jose, CA, February 1993.Google Scholar
  16. 16.
    R. Weiss, A. Duda, and D. Gifford. Content-based access to algebraic video. In IEEE Int’l Conf. on Multimedia Computing and Systems, Boston, USA, 1994.Google Scholar
  17. 17.
    A. Yoshitaka, Y. Hosoda, M. Yoshimisu, M. Hirakawa, and T. Ichikawa. Violone: Video retrieval by motion example. Visual Languages and Computing, 7, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Duc A. Tran
    • 1
  • Kien A. Hua
    • 1
  • Khanh Vu
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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