Advertisement

Incremental Learning for Interactive Sketch Recognition

  • Achraf Ghorbel
  • Abdullah Almaksour
  • Aurélie Lemaitre
  • Eric Anquetil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7423)

Abstract

In this paper, we present the integration of a classifier, based on an incremental learning method, in an interactive sketch analyzer. The classifier recognizes the symbol with a degree of confidence. Sometimes the analyzer considers that the response is insufficient to make the right decision. The decision process then solicits the user to explicitly validate the right decision. The user associates the symbol to an existing class, to a newly created class or ignores this recognition. The classifier learns during the interpretation phase. We can thus have a method for auto-evolutionary interpretation of sketches. In fact, the user participation has a great impact to avoid error accumulation during the analysis. This paper demonstrates this integration in an interactive method based on a competitive breadth-first exploration of the analysis tree for interpreting the 2D architectural floor plans.

Keywords

Fuzzy Inference System Production Rule Membership Degree User Intervention Incremental Learning 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chan, K.F., Yeung, D.Y.: An efficient syntactic approach to structural analysis of on-line handwritten mathematical expressions. Pattern Recognition 33(3), 375–384 (2000)CrossRefGoogle Scholar
  2. 2.
    Fitzgerald, J.A., Geiselbrechtinger, F., Kechadi, T.: Mathpad: A fuzzy logic-based recognition system for handwritten mathematics. In: ICDAR 2007 (2007)Google Scholar
  3. 3.
    Mao, S., Rosenfeld, A., Kanungo, T.: Document structure analysis algorithms: a literature survey. In: Proc. SPIE Electronic Imaging, vol. 5010, pp. 197–207 (2003)Google Scholar
  4. 4.
    Coüasnon, B.: Dmos, a generic document recognition method: Application to table structure analysis in a general and in a specific way. In: IJDAR 2006, vol. 8(2) (2006)Google Scholar
  5. 5.
    Ghorbel, A., Macé, S., Lemaitre, A., Anquetil, E.: Interactive competitive breadth-first exploration for sketch interpretation. In: ICDAR, pp. 1195–1199 (2011)Google Scholar
  6. 6.
    Macé, S., Anquetil, E.: Eager interpretation of on-line hand-drawn structured documents: The dali methodology. Pattern Recognition, 3202–3214 (2009)Google Scholar
  7. 7.
    Almaksour, A., Anquetil, E.: Improving premise structure in evolving takagi-sugeno neuro-fuzzy classifiers. Evolving Systems 2, 25–33 (2011)CrossRefGoogle Scholar
  8. 8.
    Angelov, P.P., Filev, D.P.: An approach to online identification of takagi-sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics 34, 484–498 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Achraf Ghorbel
    • 1
  • Abdullah Almaksour
    • 2
    • 3
    • 2
  • Aurélie Lemaitre
    • 2
    • 3
    • 2
  • Eric Anquetil
    • 1
  1. 1.INSA de RennesFrance
  2. 2.Université Européenne de BretagneFrance
  3. 3.UMR IRISARennesFrance

Personalised recommendations