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  • Research Article
  • Open Access

Semantic Identification: Balancing between Complexity and Validity

EURASIP Journal on Advances in Signal Processing20062006:041716

https://doi.org/10.1155/ASP/2006/41716

  • Received: 1 September 2004
  • Accepted: 9 May 2005
  • Published:

Abstract

An efficient scheme for identifying semantic entities within data sets such as multimedia documents, scenes, signals, and so forth, is proposed in this work. Expression of semantic entities in terms of syntactic properties is modelled with appropriately defined finite automata, which also model the identification procedure. Based on the structure and properties of these automata, formal definitions of attained validity and certainty and also required complexity are defined as metrics of identification efficiency. The main contribution of the paper relies on organizing the identification and search procedure in a way that maximizes its validity for bounded complexity budgets and reversely minimizes computational complexity for a given required validity threshold. The associated optimization problem is solved by using dynamic programming. Finally, a set of experiments provides insight to the introduced theoretical framework.

Keywords

  • Information Technology
  • Computational Complexity
  • Dynamic Programming
  • Quantum Information
  • Identification Procedure

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Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GR, 541 24, Greece

References

  1. Barnard K, Duygulu P, Forsyth D, de Freitas N, Blei DM, Jordan MI: Matching words and pictures. Journal of Machine Learning Research 2003, 3(7):1107-1135.MATHGoogle Scholar
  2. Wallace M, Avrithis Y, Stamou G, Kollias S: Knowledge-based multimedia content indexing and retrieval. In Multimedia Content and Semantic Web: Methods, Standards and Tools. Edited by: Stamou G, Kollias S. John Wiley & Sons, New York, NY, USA; 2005.Google Scholar
  3. Dorado A, Izquierdo E: Semantic labeling of images combining color, texture and keywords. Proceeding of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 3: 9-12.Google Scholar
  4. Lew MS: Next-generation web searches for visual content. IEEE Computer 2000, 33(11):46-53. 10.1109/2.881694View ArticleGoogle Scholar
  5. Manjunath BS, Salembier P, Sikora T (Eds): Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, New York, NY, USA; 2002.Google Scholar
  6. Sikora T: The MPEG-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology 2001, 11(6):696-702. 10.1109/76.927422MathSciNetView ArticleGoogle Scholar
  7. Visser R, Sebe N, Lew MS: Detecting automobiles and people for semantic video retrieval. Proceeding of 16th International Conference on Pattern Recognition (ICPR '02), August 2002, Quebec City, Canada 2: 733-736.Google Scholar
  8. Duygulu P, Barnard K, de Freitas N, Forsyth DA: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. Proceeding of 7th European Conference on Computer Vision (ECCV '02), May 2002, Copenhagen, Denmark 4: 97-112.MATHGoogle Scholar
  9. Akrivas G, Stamou GB, Kollias S: Semantic association of multimedia document descriptions through fuzzy relational algebra and fuzzy reasoning. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 2004, 34(2):190-196. 10.1109/TSMCA.2003.819498View ArticleGoogle Scholar
  10. Wallace M, Kollias S: Computationally efficient incremental transitive closure of sparse fuzzy binary relations. Proceeding of IEEE International Conference on Fuzzy Systems (IEEE-FUZZ '04), July 2004, Budapest, HungaryGoogle Scholar
  11. Avrithis Y, Stamou G, Wallace M, et al.: Unified access to heterogeneous audiovisual archives. Journal of Universal Computer Science 2003, 9(6):510-519.Google Scholar
  12. Klir GJ, Yuan B: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, Upper Saddle River, NJ, USA; 1995.MATHGoogle Scholar
  13. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (Eds): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York, NY, USA; 2003.MATHGoogle Scholar
  14. Straccia U: Reasoning within fuzzy description logics. Journal of Artificial Intelligence Research January–June 2001, 14: 137-166.MathSciNetMATHGoogle Scholar
  15. Fellbaum C (Ed): WordNet: An Electronic Lexical Database. MIT Press, Cambridge, Mass, USA; 1998.MATHGoogle Scholar
  16. Lewis HR, Papadimitriou CH: Elements of the Theory of Computation. Prentice-Hall, Upper Saddle River, NJ, USA; 1998.Google Scholar
  17. Kelleler H, Pferschy U, Pisinger D: Knapsack Problems. Springer, Berlin, Germany; 2004.View ArticleMATHGoogle Scholar
  18. Bellman RE: Dynamic Programming. Princeton University Press, Princeton, NJ, USA; 1957.MATHGoogle Scholar
  19. Bretthauer KM, Shetty B: The nonlinear knapsack problem—algorithms and applications. European Journal of Operational Research 2002, 138(3):459-472. 10.1016/S0377-2217(01)00179-5MathSciNetView ArticleMATHGoogle Scholar
  20. Assfalg J, Bertini M, Colombo C, Del Bimbo A: Semantic annotation of sports videos. IEEE Multimedia 2002, 9(2):52-60. 10.1109/93.998060View ArticleMATHGoogle Scholar
  21. Leonardi R, Migliorati P, Prandini M: Semantic indexing of sports program sequences by audio-visual analysis. Proceeding of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 1: 9-12.Google Scholar
  22. Xie L, Xu P, Chang S-F, Divakaran A, Sun H: Structure analysis of soccer video with domain knowledge and hidden Markov models. Pattern Recognition Letters 2004, 25(7):767-775. 10.1016/j.patrec.2004.01.005View ArticleGoogle Scholar
  23. Tsechpenakis G, Xirouhakis Y, Delopoulos A: Main mobile object detection and localization in video sequences. Proceeding of 4th International Conference on Advances in Visual Information Systems (VISUAL '00), November 2000, Lyon, France, Lecture Notes in Computer Science 1929: 84-95.Google Scholar

Copyright

© Falelakis et al. 2006

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