Multimedia Tools and Applications

, Volume 22, Issue 3, pp 215–234 | Cite as

Web Access to Large Audiovisual Assets Based on User Preferences

  • K. Karpouzis
  • G. Moschovitis
  • K. Ntalianis
  • S. Ioannou
  • S. Kollias


Current multimedia databases contain a wealth of information in the form of audiovisual as well as text data. Even though efficient search algorithms have been developed for either media, there still exists the need for abstract presentation and summarization of the results of database users' queries. Moreover, multimedia retrieval systems should be capable of providing the user with additional information related to the specific subject of the query, as well as suggest other topics which could be identified to attract the interest of users with a similar profile. In this paper, we present solutions to these issues, giving as an example an integrated architecture we have developed, along with notions that support efficient and secure Internet access to audiovisual/video databases. Segmentation of each video in shots is followed by shot classification in a number of predetermined categories. Generation of users' profiles according to the categories, enhanced by relevance feedback, permits an efficient presentation of retrieved video shots or characteristic frames in terms of the user interest in them. Moreover, this clustering scheme assists the notion of ‘lateral’ links that enable the user to continue retrieval with data of similar nature or content to those already returned. Furthermore, user groups are formed and modeled by registering actual preferences and practices. This enables the system to ‘predict’ information that is possibly relevant to the user's interest and present it along with the returned results. The concepts utilized in this system can be smoothly integrated in MPEG-7 compatible multimedia database systems.

multimedia databases web access video summarization dynamic search user profiling query expansion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    G. Akrivas, N. Doulamis, A. Doulamis, and S. Kollias, “Scene detection methods for MPEG-encoded video signals,” in Proceeding of the 10th IEEE Mediterranean Electrotechnical Conference, Nicosia, Cyprus, July 2000, pp. 677–680.Google Scholar
  2. 2.
    Y. Avrithis, A. Doulamis, N. Doulamis, and S. Kollias, “A Stochastic framework for optimal key frame extraction from MPEG video databases,” Computer Vision and Image Understanding, Vol. 75,Nos. 1/2, pp. 3–24, 1999.Google Scholar
  3. 3.
    M. Balabanovic and Y. Shoham, “Fab: Content-based collaborative recommendation,” Communications of the ACM, Vol. 40,No. 3, pp. 66–72, 1997.Google Scholar
  4. 4.
    C. Basu, H. Hirsh, and W. Cohen, “Recommendation as classification: Using social and content-based information in recommendation,” in Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI, pp. 714–720, 1998.Google Scholar
  5. 5.
    Yeo Boon-Lock and Liu Bede “Rapid scene analysis on compressed video,” IEEE Circuits and Systems on Video Technology, Vol. 5,No. 6, pp. 533–544, 1995.Google Scholar
  6. 6.
    A. Doulamis, Y. Avrithis, N. Doulamis, and S. Kollias, “Interactive content-based retrieval in video databases using fuzzy classification and relevance feedback,” in Proceedings of IEEE International Conference on Multimedia Computing and Systems (ICMSC '99), Florence, Italy, Vol. 2, pp. 954–958.Google Scholar
  7. 7.
    W.B. Frakes, “Stemming algorithms,” in Information Retrieval Data Structures and Algorithms, Prentice Hall: Upper Saddle River, NJ, USA, 1992, pp. 131–160.Google Scholar
  8. 8.
    Simon S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall: Upper Saddle River, NJ, USA, 1998.Google Scholar
  9. 9.
    Hill, Stead, “Rosenstein & Furnas recommending and evaluating choices in a virtual community of use,” in Proceedings of CHII95 Conference on Human Factors in Computing Systems, ACM Press, 1995.Google Scholar
  10. 10.
    IST Program, Unified Intelligent Access to Heterogeneous Audiovisual Content (FAETHON) 2001–2003. Scholar
  11. 11.
    R. Koenen and F. Pereira, “MPEG-7: A standardised description of audiovisual content,” Signal Processing: Image Communication, Vol. 16,No. 1–2, pp. 5–13, Sept. 2000.Google Scholar
  12. 12.
    Y. Kotsanis, Y. Maistros, and A. Zavras, “Quicklem: A software system for Greek word-class determination,” Literary and Linguistic Computing, Oxford University Press, 1987, Vol. 2,No. 4.Google Scholar
  13. 13.
    M. Montebello, “Optimizing recall/precision scores in IR over the WWW,” in Proceedings of the 21st ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press: New York, NY, USA, 1998, pp. 361–362.Google Scholar
  14. 14.
    A. Moukas and P. Maes, “Amalthaea: Evolving multi-agent information filtering and discovery systems for the WWW,” Autonomous Agents and Multi-Agent Systems, 1998, Vol. 1, pp. 59–88.Google Scholar
  15. 15.
    M. Pazzani, J. Muramatsu, and D. Billsus, “Syskill & Webert: Identifying interesting web sites,” in Proceedings of the National Conference on AI, AAAI Press: Menlo Park, California, USA, 1996, pp. 54–61.Google Scholar
  16. 16.
    InProceedings of International Workshop on Very Low Bitrate Video Coding, (VLBV01), Stefanos Kollias (Ed.), Athens, Greece, 2001. Scholar
  17. 17.
    P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An open architecture for collaborative filtering of netnews,” in Proceedings of the ACM Conference on Computer-Supported Cooperative Work, ACM Press: New York, NY, 1994, pp. 175–186.Google Scholar
  18. 18.
    E. Rich, “Users are individuals: Individualizing user models,” International Journal of Man-Machine Studies, Vol. 18, pp. 199–214, 1983.Google Scholar
  19. 19.
    R. Rivest, “The MD5 message-digest algorithm,” RFC 1321. Scholar
  20. 20.
    J. Rocchio Jr., “Relevance feedback in information retrieval,” The SMART Retrieval System-Experiments in Automatic Document Processing, Prentice Hall: Upper Saddle River, NJ, USA, 1971, pp. 313–323.Google Scholar
  21. 21.
    P. Salembier, R. Qian, N. O'Connor, P. Correia, I. Sezan, and P. van Beek, “Description schemes for video programs, Users and Devices,” Signal Processing: Image Communication, Vol. 16, Nos. 1–2, pp. 211–234, Sept. 2000.Google Scholar
  22. 22.
    G. Salton and C. Buckley, “Term weighting approaches in automatic text retrieval,” Information Processing & Management, Vol. 24,No. 5, pp. 513–523, 1988.Google Scholar
  23. 23.
    M. Sanderson, “Retrieving with good sense,” Information Retrieval, Vol. 2,No. 1, pp. 47–67, 2000.Google Scholar
  24. 24.
    W. Simpson, PPP Challenge Handshake Authentication Protocol (CHAP) RFC 1994.Google Scholar
  25. 25.
    U. Shardanand and P. Maes, “Social information filtering: Algorithms for automating Word of Mouth,” in Proceedings of the CHI '95, Denver, CO. May 1995.Google Scholar
  26. 26.
    N. Tsapatsoulis, Y. Avrithis, and S. Kollias, “Efficient face detection for multimedia applications,” in Proceedings of the ICIP'00, Vancouver, BC, Canada, Sept. 2000, Vol. 2, pp. 247–250.Google Scholar
  27. 27.
    N. Tsapatsoulis, Y. Avrithis, and S. Kollias, “Facial image indexing in multimedia databases, Pattern Analysis and Applications,” Special Issue on Image Indexation, Springer-Verlag, 2001, Vol. 4,No. 2/3, pp. 93–107.Google Scholar
  28. 28.
    T.W. Yan and H. Garcia-Molina, “SIFT—A tool for wide-area information dissemination,” in Proceedings of the USENIX Technical Conference, USENIX, Berkeley, CA, USA, 1995, pp. 177–186.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • K. Karpouzis
    • 1
  • G. Moschovitis
    • 1
  • K. Ntalianis
    • 1
  • S. Ioannou
    • 1
  • S. Kollias
    • 1
  1. 1.Image, Video and Multimedia Laboratory, Electrical and Computer Engineering DepartmentNational Technical University of AthensAthensGreece

Personalised recommendations