A Generic Description of the Concept Lattices’ Classifier: Application to Symbol Recognition

  • Stéphanie Guillas
  • Karell Bertet
  • Jean-Marc Ogier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3926)


In this paper, we present the problem of noisy images recognition and in particular the stage of primitives selection in a classification process. We suppose that segmentation and statistical features extraction on documentary images are realized. We describe precisely the use of concept lattice and compare it with a decision tree in a recognition process. From the experimental results, it appears that concept lattice is more adapted to the context of noisy images.


Decision Tree Recognition Rate Noisy Image Concept Lattice Formal Concept Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adam, S., et al.: Multi-scaled and multi oriented character recognition: An original strategy. In: ICDAR 1999, September 1999, pp. 45–48 (1999)Google Scholar
  2. 2.
    Tombre, K., Lamiroy, B.: Graphics recognition - from re-engineering to retrieval. In: Proceedings of 7th ICDAR, Edinburgh (Scotland, UK), August 2003, pp. 148–155 (2003)Google Scholar
  3. 3.
    Canu, S., Smola, A.: Kernel methods and the exponential family. In: National ICT (2005)Google Scholar
  4. 4.
    Bunke, H.: Recent developments in graph matching. In: 15th International Conference on Pattern Recognition, vol. 2, pp. 117–124 (2000)Google Scholar
  5. 5.
    Lefevre, E., Colot, O., Vannoorenberghe, P., De Brucq, D.: Contribution des mesures d’information á la modélisation crédibiliste de connaissances. Traitement du Signal 17(2) (2000)Google Scholar
  6. 6.
    Milgram, M.: Reconnaissance des formes: méthodes numériques et connexionnistes. Colin, A. (1993)Google Scholar
  7. 7.
    Gunes, V., Menard, M., Loonis, P.: A multiple classifier system using ambiguity rejection for clustering-classification cooperation. In: IJUFKS. World Scientific, Singapore (2000)Google Scholar
  8. 8.
    Birkhoff, G.: Lattice theory, 3rd edn., vol. 25. American Mathematical Society, Providence (1967)MATHGoogle Scholar
  9. 9.
    Ganter, B., Wille, R.: Formal concept analysis, Mathematical foundations. Springer, Heidelberg (1999)CrossRefMATHGoogle Scholar
  10. 10.
    Bordat, J.: Calcul pratique du treillis de Galois d’une correspondance. Math. Sci. Hum. 96, 31–47 (1986)MathSciNetMATHGoogle Scholar
  11. 11.
    Godin, R., Mili, H.: Building and maintening analysis-level class hierarchies using Galois lattices. In: OOPSLA, pp. 394–410 (1993)Google Scholar
  12. 12.
    Nourine, L., Raynaud, O.: A fast algorithm for building lattices. In: Third International Conference on Orders, Algorithms and Applications, Montpellier, France (August 1999)Google Scholar
  13. 13.
    Bertet, K., Nebut, M.: Efficient algorithms on the family associated to an implicationnal system. DMTCS 6(2), 315–338 (2004)MathSciNetMATHGoogle Scholar
  14. 14.
    Taouil, R., Bastide, Y.: Computing proper implications. In: Proceedings of ICCS 2001 Internationnal Workshop on Concept Lattices-Based Theory, Methods and tools for Knowledge Discovery in Databases, pp. 290–303 (2001)Google Scholar
  15. 15.
    Obiedkov, S., Duquenne, V.: Incremental construction of the canonical implication basis. In: Fourth International Conference Journée de l’Informatique messine, pp. 15–23 (2003) (Submitted to Discrete Applied Mathematics)Google Scholar
  16. 16.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefMATHGoogle Scholar
  17. 17.
  18. 18.
    Kanungo, T., et al.: Document degradation models: parameter estimation and model validation. In: IAPR Workshop on machine vision applications, Kawasaki (Japan), pp. 552–557 (1994)Google Scholar
  19. 19.
    Derrode, S., Daoudi, M., Ghorbel, F.: Invariant content-based image retrieval using a complete set of fourier-mellin descriptors. In: Int. Conf. on Multimedia Computing and Systems (ICMCS 1999), June 1999, pp. 877–881 (999)Google Scholar
  20. 20.
    Tabbone, S., Wendling, L.: Recherche d’images par le contenu l’aide de la transforme de radon. In: Technique et Science Informatiques (2003)Google Scholar
  21. 21.
    Teague, M.: Image analysis via the general theory of moments. Journal of Optical Society of America (JOSA) 70, 920–930 (2003)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stéphanie Guillas
    • 1
  • Karell Bertet
    • 1
  • Jean-Marc Ogier
    • 1
  1. 1.L3IUniversité de La RochelleLa RochelleFrance

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