Trends and Issues in Description Logics Frameworks for Image Interpretation

  • Stamatia Dasiopoulou
  • Ioannis Kompatsiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6040)

Abstract

Description Logics have recently attracted significant interest as the underlying formalism for conceptual modelling in the context of high-level image interpretation. Differences in the formulation of image interpretation semantics have resulted in varying configurations with respect to the adopted modelling paradigm, the utilised form of reasoning, and the way imprecision is managed. In this paper, we examine the relevant literature, outlining the corresponding strengths and weaknesses, and argue that although coming up with a complete solution is hard to envisage any time soon, there are a number of key considerations that may serve as guidelines towards this direction.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The description logic handbook: Theory, implementation, and applications. In: Description Logic Handbook. Cambridge University Press, Cambridge (2003)Google Scholar
  2. 2.
    Patel-Schneider, P., Horrocks, I.: A comparison of two modelling paradigms in the semantic web. J. Web Sem. 5(4), 240–250 (2007)Google Scholar
  3. 3.
    Little, S., Hunter, J.: Rules-by-example - a novel approach to semantic indexing and querying of images. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 534–548. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Schober, J.P., Hermes, T., Herzog, O.: Content-based image retrieval by ontology-based object recognition. In: KI 2004 Workshop on Applications of Description Logics (ADL), Ulm, Germany september 24, pp. 1–10 (2004)Google Scholar
  5. 5.
    Neumann, B., Moller, R.: On scene interpretation with description logics FBI-B-257/04 (2004)Google Scholar
  6. 6.
    Simou, N., Athanasiadis, T., Tzouvaras, V., Kollias, S.: Multimedia reasoning with f-shin. In: 2nd International Workshop on Semantic Media Adaptation and Personalization, London, UK, pp. 413–420 (2007)Google Scholar
  7. 7.
    Hudelot, C., Atif, J., Bloch, I.: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets and Systems 159(15), 1929–1951 (2008)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  9. 9.
    Hanjalic, A., Lienhart, R., Ma, W., Smith, J.: The holy grail of multimedia information retrieval: So close or yet so far away. IEEE Proceedings, Special Issue on Multimedia Information Retrieval 96(4), 541–547 (2008)Google Scholar
  10. 10.
    Shanahan, M.: A logical account of perception incorporating feedback and expectation. In: International Conference on Principles and Knowledge Representation and Reasoning (KR 2002), Toulouse, France, April, 22-25 pp. 3–13 (2002)Google Scholar
  11. 11.
    Hotz, L., Neumann, B., Terzic, K.: High-level expectations for low-level image processing. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 87–94. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Dasiopoulou, S., Mezaris, V., Kompatsiaris, I., Papastathis, V., Strintzis, M.: Knowledge-assisted semantic video object detection. IEEE Trans. Circuits Syst. Video Techn. 15(10), 1210–1224 (2005)CrossRefGoogle Scholar
  13. 13.
    Bagdanov, A., Bertini, M., DelBimbo, A., Serra, G., Torniai, C.: Semantic annotation and retrieval of video events using multimedia ontologies. In: IEEE International Conference on Semantic Computing (ICSC), Irvine, CA, USA, pp. 713–720 (2007)Google Scholar
  14. 14.
    Moller, R., Neumann, B., Wessel, M.: Towards computer vision with description logics: Some recent progress. In: Workshop on Integration of Speech and Image Understanding, Corfu, Greece, pp. 101–115 (1999)Google Scholar
  15. 15.
    Hunter, J., Drennan, J., Little, S.: Realizing the hydrogen economy through semantic web technologies. IEEE Intelligent Systems Journal - Special Issue on eScience 19, 40–47 (2004)Google Scholar
  16. 16.
    Dasiopoulou, S., Kompatsiaris, I., Strintzis, M.: Applying fuzzy dls in the extraction of image semantics. J. Data Semantics 14, 105–132 (2009)Google Scholar
  17. 17.
    Snoek, C., Huurnink, B., Hollink, L., Rijke, M., Schreiber, G., Worring, M.: Adding semantics to detectors for video retrieval. IEEE Transactions on Multimedia 9(5), 975–986 (2007)CrossRefGoogle Scholar
  18. 18.
    Shanahan, M.: Robotics and the common sense informatic situation. In: European Conference on Artificial Intelligence (ECAI), Budapest, Hungary, August, 11-16, pp. 684–688 (1996)Google Scholar
  19. 19.
    Shanahan, M.: Perception as abduction: Turning sensor data into meaningful representation. Cognitive Science 29(1), 103–134 (2005)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Espinosa, S., Kaya, A., Melzer, S., Möller, R., Wessel, M.: Multimedia interpretation as abduction. In: International Workshop on Description Logics (DL), Brixen-Bressanone, Italy, June, 8-10, pp. 323–331 (2007)Google Scholar
  21. 21.
    Elsenbroich, C., Kutz, O., Sattler, U.: A case for abductive reasoning over ontologies. In: Workshop on OWL: Experiences and Directions (OWLED), Athens, Georgia, USA. (November 10-11, 2006)Google Scholar
  22. 22.
    Mayer, M., Pirri, F.: First order abduction via tableau and sequent calculi. Logic Journal of the IGPL 1(1), 99–117 (1993)MATHCrossRefGoogle Scholar
  23. 23.
    Sciascio, E.D., Donini, F.: Description logics for image recognition: a preliminary proposal. In: International Workshop on Description Logics (DL), Linköping, Sweden (July 30- August 1, 1999)Google Scholar
  24. 24.
    Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  25. 25.
    Rabiner, L., Juang, B.: An introduction to hidden markov models. ASSP Magazine, IEEE [see also IEEE Signal Processing Magazine] 3(1), 4–16 (1986)Google Scholar
  26. 26.
    Heckerman, D.: A tutorial on learning with bayesian networks. Learning in Graphical Models, 301–354 (1998)Google Scholar
  27. 27.
    Neumann, B., Weiss, T.: Navigating through logic-based scene models for high-level scene interpretations. In: ICVS, pp. 212–222 (2003)Google Scholar
  28. 28.
    Hollink, L., Little, S., Hunter, J.: Evaluating the application of semantic inferencing rules to image annotation. In: International Conference on Knowledge Capture (K-CAP), Banff, Alberta, Canada, October 2-5 pp. 91–98 (2005)Google Scholar
  29. 29.
    Nilsson, N.: Probabilistic logic. Artif. Intell. 28(1), 71–87 (1986)MATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic: Theory and applications. Prentice-Hall, Englewood Cliffs (1995)MATHGoogle Scholar
  31. 31.
    Giugno, R., Lukasiewicz, T.: P-shoq(d): A probabilistic extension of shoq(d) for probabilistic ontologies in the semantic web. In: European Conference on Logics in Artificial Intelligence (JELIA), Cosenza, Italy September 23-26, pp. 86–97 (2002)Google Scholar
  32. 32.
    Lukasiewicz, T.: Expressive probabilistic description logics. Artif. Intell. 172(6-7), 852–883 (2008)MATHCrossRefMathSciNetGoogle Scholar
  33. 33.
    Klinov, P.: Pronto: A non-monotonic probabilistic description logic reasoner. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 822–826. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  34. 34.
    Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., Katz, Y.: Pellet: A practical owl-dl reasoner. J. Web Sem. 5(2), 51–53 (2007)Google Scholar
  35. 35.
    Town, C., Sinclair, D.: A self-referential perceptual inference framework for video interpretation. In: International Confernce on Computer Vision Systems (ICVS), Graz, Austria, pp. 54–67 (2003)Google Scholar
  36. 36.
    Neumann, B., Möller, R.: On scene interpretation with description logics. Image Vision Comput. 26(1), 82–101 (2008)CrossRefGoogle Scholar
  37. 37.
    da Costa, P., Laskey, K., Laskey, K.: Pr-owl: A bayesian ontology language for the semantic web. In: URSW. LNCS, pp. 88–107 (2008)Google Scholar
  38. 38.
    Ding, Z.: BayesOWL: A Probabilistic Framework for Semantic Web. Phd thesis, University of Maryland, Baltimore County (December 2005)Google Scholar
  39. 39.
    Yen, J.: Generalizing term subsumption languages to fuzzy logic. In: 12th International Joint Conference on Artificial Intelligence (IJCAI), Sydney, Australia, August 24-30, pp. 472–477 (1991)Google Scholar
  40. 40.
    Straccia, U.: Reasoning within fuzzy description logics. J. Artif. Intell. Res (JAIR) 14, 137–166 (2001)MATHMathSciNetGoogle Scholar
  41. 41.
    Straccia, U.: Transforming fuzzy description logics into classical description logics. In: European Conference on Logics in Artificial Intelligence (JELIA), Lisbon, Portugal, September, 27-30 pp. 385–399 (2004)Google Scholar
  42. 42.
    Stoilos, G., Stamou, G., Tzouvaras, V., Pan, J., Horrocks, I.: The fuzzy description logic f-SHIN. In: International Workshop on Uncertainty Reasoning For the Semantic Web (URSW), Galway, Ireland November, 7 pp. 67–76 (2005)Google Scholar
  43. 43.
    Bobillo, F., Straccia, U.: fuzzydl: An expressive fuzzy description logic reasoner. In: International Conference on Fuzzy Systems (FUZZ) June, 1-6 pp. 923–930. IEEE Computer Society, Hong Kong (2008)Google Scholar
  44. 44.
    Simou, N., Kollias, S.: Fire: A fuzzy reasoning engine for impecise knowledge, In: K-Space PhD Students Workshop Berlin, Germany, September 14 (2007)Google Scholar
  45. 45.
    Bobillo, F., Delgado, M., Gómez-Romero, J.: Delorean: A reasoner for fuzzy owl 1.1. In: International Workshop on Uncertainty Reasoning for the Semantic Web (URSW), Karlsruhe, Germany, October 26 (2008)Google Scholar
  46. 46.
    Simou, N., Athanasiadis, T., Stoilos, G., Kollias, S.: Image indexing and retrieval using expressive fuzzy description logics. Signal, Image and Video Processing 2(4), 321–335 (2008)CrossRefGoogle Scholar
  47. 47.
    Straccia, U.: Managing uncertainty and vagueness in description logics, logic programs and description logic programs. In: Tutorial Lectures, Reasoning Web, 4th International Summer School, Venice, Italy, pp. 54–103 (2008)Google Scholar
  48. 48.
    Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued logics: A clarification. Annals of Mathematics and Artificail Intelligence 32(1-4), 35–66 (2001)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stamatia Dasiopoulou
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Informatics and Telematics InstituteCentre for Research and Technology HellasThessalonikiGreece

Personalised recommendations