Advertisement

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

  • Muhammad Muzzamil Luqman
  • Mathieu Delalandre
  • Thierry Brouard
  • Jean-Yves Ramel
  • Josep Lladós
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)

Abstract

The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols.

Keywords

symbol recognition overlapping fuzzy interval structural signature Bayesian network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Llados, J., Sanchez, G.: Symbol recognition using graphs. In: ICIP, pp. 49–52 (2003)Google Scholar
  2. 2.
    Chhabra, A.K.: Graphic symbol recognition: An overview. In: Chhabra, A.K., Tombre, K. (eds.) GREC 1997. LNCS, vol. 1389, pp. 68–79. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Llados, J., Valveny, E., Sanchez, G., Marti, E.: Symbol recognition: Current advances and perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–128. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Cordella, L.P., Vento, M.: Symbol recognition in documents: a collection of techniques? IJDAR 3(2), 73–88 (2000)CrossRefGoogle Scholar
  5. 5.
    Tombre, K., Tabbone, S., Dosch, P.: Musings on symbol recognition. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 23–34. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Bunke, H., Gunter, S., Jiang, X.: Towards bridging the gap between statistical and structural pattern recognition: Two new concepts in graph matching. In: Singh, S., Murshed, N., Kropatsch, W.G. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 1–11. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Delalandre, M., Trupin, É., Ogier, J.M.: Symbols recognition system for graphic documents combining global structural approaches and using a XML representation of data. In: Fred, A.L.N., Caelli, T., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 425–433. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Hse, H., Newton, A.R.: Sketched symbol recognition using zernike moments. In: ICPR, pp. 367–370 (2004)Google Scholar
  9. 9.
    Barrat, S., Tabbone, S., Nourrissier, P.: A bayesian classifier for symbol recognition. In: GREC, October 24, vol. 7 (2007)Google Scholar
  10. 10.
    Rusinol, M.: Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections. PhD thesis, Universitat Autonoma de Barcelona (2009)Google Scholar
  11. 11.
    Qureshi, R.J., Ramel, J.Y., Cardot, H., Mukherji, P.: Combination of symbolic and statistical features for symbols recognition. In: ICSCN, pp. 477–482 (2007)Google Scholar
  12. 12.
    Zhang, W., Liu, W.: A new vectorial signature for quick symbol indexing, filtering and recognition. In: ICDAR, vol. 9, pp. 536–540 (2007)Google Scholar
  13. 13.
    Rusiñol, M., Lladós, J.: Symbol spotting in technical drawings using vectorial signatures. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 35–46. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Dosch, P., Llados, J.: Vectorial signatures for symbol discrimination. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 154–165. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Coustaty, M., Guillas, S., Visani, M., Bertet, K., Ogier, J.M.: On the joint use of a structural signature and a galois lattice classifier for symbol recognition. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 61–70. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Luqman, M.M., Brouard, T., Ramel, J.Y.: Graphic symbol recognition using graph based signature and bayesian network classifier. In: ICDAR, vol. 10, pp. 1325–1329 (2009)Google Scholar
  17. 17.
    Ventura, A., Schettini, R.: Graphic symbol recognition using a signature technique. In: ICPR, vol. 2, pp. 533–535 (1994)Google Scholar
  18. 18.
    Valveny, E., Marti, E.: Deformable template matching within a bayesian framework for hand-written graphic symbol recognition. In: Chhabra, A.K., Dori, D. (eds.) GREC 1999. LNCS, vol. 1941, pp. 193–208. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Mitra, S., Pal, S.K.: Fuzzy sets in pattern recognition and machine intelligence. FSS 156(3), 381–386 (2005)MathSciNetGoogle Scholar
  20. 20.
    Charniak, E.: Bayesian networks without tears. AI Magazine 12(4), 50–63 (1991)Google Scholar
  21. 21.
    Heckerman, D.: A tutorial on learning with bayesian networks. In: Innovations in Bayesian Networks. SCI, vol. 156, pp. 33–82 (2008)Google Scholar
  22. 22.
    Delaplace, A., Brouard, T., Cardot, H.: Two evolutionary methods for learning bayesian network structures. In: Wang, Y., Cheung, Y.-m., Liu, H. (eds.) CIS 2006. LNCS (LNAI), vol. 4456, pp. 288–297. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Leray, P., François, O.: BNT structure learning package: Documentation and experiments. Technical report, Laboratoire PSI - INSA Rouen- FRE CNRS 2645 (November 2004)Google Scholar
  24. 24.
    Colot, O., Olivier, C., Courtellemont, P., El Matouat, A.: Information criteria and abrupt changes in probability laws. In: Signal Processing VII: Theories and Applications, pp. 1855–1858 (1994)Google Scholar
  25. 25.
    Aksoy, S., Ye, M., Schauf, M., Song, M., Wang, Y., Haralick, R.M., Parker, J.R., Pivovarov, J., Royko, D., Sun, C., Farneback, G.: Algorithm performance contest. In: ICPR, vol. IV, pp. 870–876 (2000)Google Scholar
  26. 26.
    Valveny, E., Dosch, P.: Symbol recognition contest: A synthesis. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 368–385. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Dosch, P., Valveny, E.: Report on the second symbol recognition contest. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 381–397. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Valveny, E., Dosch, P., Fornes, A., Escalera, S.: Report on the third contest on symbol recognition. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 321–328. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    http://www.imagemagick.org/ (As on March 20, 2010)
  30. 30.
    http://www.qgar.org/ (As on March 20, 2010)
  31. 31.
  32. 32.
    Delalandre, M., Pridmore, T., Valveny, E., Locteau, H., Trupin, E.: Building synthetic graphical documents for performance evaluation. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 288–298. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  33. 33.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Muhammad Muzzamil Luqman
    • 1
    • 2
  • Mathieu Delalandre
    • 1
  • Thierry Brouard
    • 1
  • Jean-Yves Ramel
    • 1
  • Josep Lladós
    • 2
  1. 1.Laboratoire d’InformatiqueUniversité François Rabelais de ToursFrance
  2. 2.Computer Vision CenterUniversitat Autònoma de BarcelonaSpain

Personalised recommendations