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Knowledge Assisted Analysis and Categorization for Semantic Video Retrieval

  • Manolis Wallace
  • Thanos Athanasiadis
  • Yannis Avrithis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3115)

Abstract

In this paper we discuss the use of knowledge for the analysis and semantic retrieval of video. We follow a fuzzy relational approach to knowledge representation, based on which we define and extract the context of either a multimedia document or a user query. During indexing, the context of the document is utilized for the detection of objects and for automatic thematic categorization. During retrieval, the context of the query is used to clarify the exact meaning of the query terms and to meaningfully guide the process of query expansion and index matching. Indexing and retrieval tools have been implemented to demonstrate the proposed techniques and results are presented using video from audiovisual archives.

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References

  1. 1.
    Koenen, R.: Overview of the MPEG-4 Standard. ISO/IEC JTC 1/SC 29/WG 11/N4668 (March 2002)Google Scholar
  2. 2.
    Sikora, T.: The MPEG-7 Visual standard for content description - an overview. IEEE Trans. on Circuits and Systems for Video Technology 11(6), 696–702 (2001)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Avrithis, Y., Stamou, G., Delopoulos, A., Kollias, S.: Intelligent Semantic Access to Audiovisual Content. In: Vlahavas, I.P., Spyropoulos, C.D. (eds.) SETN 2002. LNCS (LNAI), vol. 2308, p. 215. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Hunter, J.: Adding Multimedia to the Semantic Web: Building an MPEG-7 Ontology. In: Proc. The First Semantic Web Working Symposium (SWWS 2001), July 2001, Stanford University, California (2001)Google Scholar
  5. 5.
    Smeulders, A., Worring, M., Santini, S.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22(12) (2000)Google Scholar
  6. 6.
    La Cascia, M., Sethi, S., Sclaroff, S.: Combining textual and visual cues for contentbased image retrieval on the world wide web. In: IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL 1998) (June 1998)Google Scholar
  7. 7.
    Stamou, G., Avrithis, Y., Kollias, S., Marques, F., Salembier, P.: Semantic Unification of Heterogenous Multimedia Archives. In: Proc. of 4th European Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2003). London, UK, April 9-11 (2003)Google Scholar
  8. 8.
    Tzouveli, P., Andreou, G., Tsechpenakis, G., Avrithis, Y., Kollias, S.: Intelligent Visual Descriptor Extraction from Video Sequences. In: Nürnberger, A., Detyniecki, M. (eds.) AMR 2003. LNCS, vol. 3094, pp. 132–146. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Wallace, M., Akrivas, G., Stamou, G.: Automatic Thematic Categorization of Documents Using a Fuzzy Taxonomy and Fuzzy Hierarchical Clustering. In: Proc. of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), St. Louis, MO, USA (May 2003)Google Scholar
  10. 10.
    Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic, Theory and Applications. Prentice Hall, New Jersey (1995)zbMATHGoogle Scholar
  11. 11.
    Miyamoto, S.: Fuzzy sets in information retrieval and cluster analysis. Kluwer Academic publishers, Dordrecht (1990)zbMATHGoogle Scholar
  12. 12.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Manolis Wallace
    • 1
  • Thanos Athanasiadis
    • 1
  • Yannis Avrithis
    • 1
  1. 1.Image, Video and Multimedia Systems Laboratory School of Electrical and Computer EngineeringNational Technical University of AthensZographouGreece

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