A protocol to characterize the descriptive power and the complementarity of shape descriptors

  • Muriel Visani
  • Oriol Ramos Terrades
  • Salvatore TabboneEmail author
Original Paper


Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades et al. in Proceedings of ICDAR’07, pp. 227–231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database.


Document analysis Shape descriptors Symbol description Performance characterization Complementarity analysis 


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  1. 1.
    Terrades, O.R., Tabbone, S., Valveny, E.: A review of shape descriptors for document analysis. In: Proceedings of the International Conference on Document Analysis and Recognition—ICDAR’07, pp. 227–231 (2007)Google Scholar
  2. 2.
    Phillips I., Chhabra A.: Empirical performance evaluation of graphics recognition systems. IEEE Trans. PAMI 21(9), 849–870 (1999)Google Scholar
  3. 3.
    Chhabra, A., Phillips, I.: The second international graphics recognition contest—raster to vector conversion: A report. In: Tombre, K., Chhabra, A.K. (eds.) Graphics recognition: Algorithms and Systems. LNCS, vol. 1389, pp. 390–410. Springer (1998)Google Scholar
  4. 4.
    Chhabra, A., Phillips, I.: Performance evaluation of line drawing recognition systems. In: Proceedings of 15th. International Conference on Pattern Recognition, vol. 4, pp. 864–869. Barcelona, Spain (2000)Google Scholar
  5. 5.
    Wenyin, L., Zhai, J., Dori, D.: Extended summary of the arc segmentation contest. In: Blostein, D., Kwon, Y.B. (eds.) Graphics Recognition: Algorithms and Applications. LNCS, vol. 2390, pp. 343–349. Springer (2002)Google Scholar
  6. 6.
    Valveny, E., Dosch, P.: Symbol recognition contest: a synthesis. In: Lladós, J., Kwon, Y.B. (eds.) Graphics Recognition Recent Advances and Perspectives. LNCS, vol. 3088, pp. 368–385, Springer (2004)Google Scholar
  7. 7.
    Dosch, P., Valveny, E.: Report on the second symbol recognition contest. In: Liu, W., Lladós, J. (eds.) Graphics Recognition. Ten Years Review and Future Perspectives. LNCS, vol. 3926, pp. 381–397. Springer (2006)Google Scholar
  8. 8.
    Trier O.D., Jain A.K., Taxt T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 41–662 (1996)Google Scholar
  9. 9.
    da Fontoura Costa L., Cesar R.M. Jr: Shape Analysis and Classification: Theory and Practice, pp. 685. CRC Press, Boca Raton (2001)zbMATHGoogle Scholar
  10. 10.
    Zhang D., Lu G.: Review of shape representation and description techniques. Pattern Recognit. 37, 1–19 (2004)zbMATHCrossRefGoogle Scholar
  11. 11.
    Tumer K., Ghosh J.: Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognit. 29(2), 314–348 (1996)CrossRefGoogle Scholar
  12. 12.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  13. 13.
    Skurichina M., Duin R.P.W.: Bagging, boosting and the random subspace method for linear classifiers. Int. J. Pattern Anal. Appl. 5(2), 121–135 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Burges C.J.C.: A tutorial on support vector machines for pattern recognition. Int. J. Data. Min. Knowl. Discov. 2(2), 1–43 (1998)Google Scholar
  15. 15.
    Kittler, J.: A framework for classifier fusion: is it still needed? In: Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, pp. 45–56. Springer-Verlag (2000)Google Scholar
  16. 16.
    Ramos O., Valveny E., Tabbone S.: Optimal classifiers fusion in a non-Bayesian probabilistic framework. IEEE Tran. PAMI 31(9), 1630–1644 (2009)Google Scholar
  17. 17.
    Terrades, O.R., Valveny, E., Tabbone, S.: On the combination of ridgelets descriptors for symbol recognition. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) Graphics Recognition. Recent Advances and New Opportunities. LNCS, vol. 5046, pp. 40–50. Springer (2008)Google Scholar
  18. 18.
    Valveny E., Dosch P., Winstanley A., Zhou Y., Yang S., Yan L., Wenyin L., Elliman D., Delalandre M., Trupin E., Adam S., Ogier J.M.: A general framework for the evaluation of symbol recognition methods. Int. J. Doc. Anal. Recognit. 9(1), 59–74 (2007)CrossRefGoogle Scholar
  19. 19.
    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.) Graphics Recognition. Recent Advances and New Opportunities. LNCS vol. 5046, pp. 288–298. Springer (2008)Google Scholar
  20. 20.
    Valveny, E., Tabbone, S., Terrades, O.R., Philippot, E.: Performance characterization of shape descriptors for symbol representation. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) Graphics Recognition. Recent Advances and New Opportunities. LNCS vol. 5046, pp. 278–287. Springer (2008)Google Scholar
  21. 21.
    Jouili, S., Tabbone, S.: Evaluation of graph matching measures for documents retrieval. In: Eighth IAPR International Workshop on Graphics Recognition (GREC 09), La Rochelle (2009)Google Scholar
  22. 22.
    Kim, W.Y., Kim, Y.S.: A new region-based shape descriptor. ISO/IEC MPEG99/M5472 Maui, Hawaii (1999)Google Scholar
  23. 23.
    Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(4), 509–522 (2002)Google Scholar
  24. 24.
    Mori G., Belongie S., Malik J.: Efficient shape matching using shape contexts. IEEE Trans. PAMI 27(11), 1832–1837 (2005)Google Scholar
  25. 25.
    Visani, M., Garcia, C., Laurent, C.: Comparing robustness of two-dimensional PCA and eigenfaces for face recognition. In: Proceedings of the International Conference on Image Analysis and Recognition (ICIAR 04) Springer LNCS 3212, 2:717–724. Porto, Portugal (2004)Google Scholar
  26. 26.
    Doddington, G., Liggett, W., Martin, A., Przybocki, M., Reynolds, D.: Sheep, Goats, Lambs and Wolves: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. In: International Conference on Spoken Language Processing (ICSLP), Sydney, USA (1998)Google Scholar
  27. 27.
    Kanungo T., Haralick R.M., Phillips I.: Nonlinear global and local document degradation models. Int. J. Imaging Syst. Technol. 5, 220–230 (1994)CrossRefGoogle Scholar
  28. 28.
    Jouili, S., Tabbone, S.: Graph matching using node signatures. In: Proceedings of the 7th workshop on graph-based representations in pattern recognition—GbRPR 2009, pp. 154–163. Venice, Italy May (2009)Google Scholar
  29. 29.
    Robles-Kelly A., Hancock E.R.: Graph edit distance from spectral seriation. IEEE Trans. PAMI 27(3), 365–378 (2005)Google Scholar
  30. 30.
    Papadopoulos, A.N., Manolopoulos, Y.: Structure-based similarity search with graph histograms. In: Proceedings of International Workshop on Similarity Search (DEXA IWOSS 99), pp. 174–178 Sep (1999)Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Muriel Visani
    • 1
  • Oriol Ramos Terrades
    • 2
  • Salvatore Tabbone
    • 3
    Email author
  1. 1.L3I, University of La RochelleLa Rochelle Cedex 1France
  2. 2.Instituto Tecnológico de InformáticaUniversidad Politécnica de ValenciaValenciaSpain
  3. 3.LORIA, University of Nancy 2Vandoeuvre-les-NancyFrance

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