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A Conceptual Framework for Automatic Text-Based Indexing and Retrieval in Digital Video Collections

  • Mohammed Belkhatir
  • Mbarek Charhad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)

Abstract

The growing need for ’intelligent’ video retrieval systems leads to new architectures combining multiple characterizations of the video content that rely on expressive frameworks while providing fully-automated indexing and retrieval processes. As a matter of fact, addressing the problem of combining modalities for video indexing and retrieval is of huge importance and the only solution for achieving significant retrieval performance. This paper presents a multi-facetted conceptual framework integrating multiple characterizations of the visual and audio contents for automatic video retrieval. It relies on an expressive representation formalism handling high-level video descriptions and a full-text query framework in an attempt to operate video indexing and retrieval beyond trivial low-level processes, keyword-annotation frameworks and state-of-the art architectures loosely-coupling visual and audio descriptions.

Keywords

Color Word Semantic Concept Video Model Video Retrieval Video Shot 
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.
    Amato, G., Mainetto, G., Savino, P.: An Approach to a Content-Based Retrieval of Multimedia Data. Multimedia Tools and Applications 7, 9–36 (1998)CrossRefGoogle Scholar
  2. 2.
    Belkhatir, M., Mulhem, P., Chiaramella, Y.: Integrating Perceptual Signal Features within a Multi-facetted Conceptual Model for Automatic Image Retrieval. In: McDonald, S., Tait, J. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 267–282. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Belkhatir, M.: Combining semantics and texture characterizations for precision-oriented automatic image retrieval. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 457–474. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Berlin, B., Kay, P.: Basic Color Terms: Their universality and Evolution. UC Press (1991)Google Scholar
  5. 5.
    Bhushan, N., et al.: The Texture Lexicon: Understanding the Categorization of Visual Texture Terms and Their Relationship to Texture Images. Cognitive Science 21(2), 219–246 (1997)CrossRefGoogle Scholar
  6. 6.
    Cohn, A., et al.: Qualitative Spatial Representation and Reasoning with the Region Connection Calculus. Geoinformatica 1, 1–44 (1997)CrossRefGoogle Scholar
  7. 7.
    Fablet, R., Bouthémy, P.: Statistical motion-based video indexing and retrieval. In: Conf. on Content-Based Multimedia Information Access, pp. 602–619 (2000)Google Scholar
  8. 8.
    Fan, J., et al.: ClassView: hierarchical video shot classification, indexing, and accessing. IEEE Transactions on Multimedia 6(1), 70–86 (2004)CrossRefGoogle Scholar
  9. 9.
    Gauvain, J.L., Lamel, L., Adda, G.: The LIMSI Broadcast News transcription system. Speech Communication 37, 89–108 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Gong, Y., Chuan, H., Xiaoyi, G.: Image Indexing and Retrieval Based on Color Histograms. Multimedia Tools and Applications II, 133–156 (1996)Google Scholar
  11. 11.
    Kokkoras, F.A., et al.: Smart VideoText: a video data model based on conceptual graphs. Multimedia Syst. 8(4), 328–338 (2002)CrossRefGoogle Scholar
  12. 12.
    Kwon, S., Narayanan, S.: Speaker Change Detection Using a New Weighted Distance Measure. In: ICSLP, pp. 16–20 (2002)Google Scholar
  13. 13.
    Lim, J.H.: Explicit query formulation with visual keywords. ACM Multimedia, 407–412 (2000)Google Scholar
  14. 14.
    Lin, C.Y., Tseng, B.L., Smith, J.R.: VideoAnnEx: IBM MPEG-7 Annotation Tool for Multimedia Indexing and Concept Learning. In: IEEE ICME (2003)Google Scholar
  15. 15.
    Nie, J.Y.: An outline of a General Model for Information Retrieval Systems. In: ACM SIGIR, pp. 495–506 (1988)Google Scholar
  16. 16.
    Smeulders, A.W.M., et al.: Content-based image retrieval at the end of the early years. IEEE PAMI 22(12), 1349–1380 (2000)Google Scholar
  17. 17.
    Sowa, J.F.: Conceptual structures: information processing in mind and machine. Addison-Wesley publishing company, London (1984)zbMATHGoogle Scholar
  18. 18.
    Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)zbMATHGoogle Scholar
  19. 19.
    Zhu, X., et al.: InsightVideo: toward hierarchical video content organization for efficient browsing, summarization and retrieval. IEEE Trans. on Multimedia 7(4), 648–666 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mohammed Belkhatir
    • 1
  • Mbarek Charhad
    • 2
  1. 1.Monash University, School of Information Technology 
  2. 2.FSM, Computer Science DepartmentTunisia

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