Skip to main content

Advertisement

Semantic Identification: Balancing between Complexity and Validity

Article metrics

  • 721 Accesses

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.

References

  1. 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.

  2. 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.

  3. 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.

  4. 4.

    Lew MS: Next-generation web searches for visual content. IEEE Computer 2000, 33(11):46–53. 10.1109/2.881694

  5. 5.

    Manjunath BS, Salembier P, Sikora T (Eds): Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, New York, NY, USA; 2002.

  6. 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. 10.1109/76.927422

  7. 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.

  8. 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.

  9. 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.2003.819498

  10. 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, Hungary

  11. 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.

  12. 12.

    Klir GJ, Yuan B: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, Upper Saddle River, NJ, USA; 1995.

  13. 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.

  14. 14.

    Straccia U: Reasoning within fuzzy description logics. Journal of Artificial Intelligence Research January–June 2001, 14: 137–166.

  15. 15.

    Fellbaum C (Ed): WordNet: An Electronic Lexical Database. MIT Press, Cambridge, Mass, USA; 1998.

  16. 16.

    Lewis HR, Papadimitriou CH: Elements of the Theory of Computation. Prentice-Hall, Upper Saddle River, NJ, USA; 1998.

  17. 17.

    Kelleler H, Pferschy U, Pisinger D: Knapsack Problems. Springer, Berlin, Germany; 2004.

  18. 18.

    Bellman RE: Dynamic Programming. Princeton University Press, Princeton, NJ, USA; 1957.

  19. 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)00179-5

  20. 20.

    Assfalg J, Bertini M, Colombo C, Del Bimbo A: Semantic annotation of sports videos. IEEE Multimedia 2002, 9(2):52–60. 10.1109/93.998060

  21. 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.

  22. 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.2004.01.005

  23. 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.

Download references

Author information

Correspondence to M. Falelakis.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Falelakis, M., Diou, C. & Delopoulos, A. Semantic Identification: Balancing between Complexity and Validity. EURASIP J. Adv. Signal Process. 2006, 041716 (2006) doi:10.1155/ASP/2006/41716

Download citation

Keywords

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