Skip to main content
  • Research Article
  • Open access
  • Published:

Semantic Identification: Balancing between Complexity and Validity

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. 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.

    MATH  Google 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. 10.1109/2.881694

    Article  Google 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. 10.1109/76.927422

    Article  Google 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.

    Article  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.

    MATH  Google 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.2003.819498

    Article  Google 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, Hungary

    Google 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.

    MATH  Google 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.

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    MATH  Google 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.

    Book  Google Scholar 

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

    MATH  Google 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)00179-5

    Article  MathSciNet  Google Scholar 

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

    Article  Google 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.2004.01.005

    Article  Google 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Falelakis.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

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

Keywords