SSPR /SPR 2000: Advances in Pattern Recognition pp 17-27 | Cite as

Adaptive Graphical Pattern Recognition Beyond Connectionist-Based Approaches

  • Giovanni Adorni
  • Stefano Cagnoni
  • Marco Gori
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper proposes a general framework for the development of a novel approach to pattern recognition which is strongly based on graphical data types. These data keep at the same time the highly structured representation of classical syntactic and structural approaches and the subsymbolic capabilities of decision-theoretic approaches, typical of connectionist and statistical models. Like for decision-theoretic models, the recognition ability is mainly gained on the basis of learning from examples, that, however, are strongly structured.

Keywords

Input Graph Connectionist Model Pattern Recognition Method Pattern Recogni Pattern Recognition Problem 
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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References

  1. 1.
    A. Aho and T.G. Peterson, “A minimum distance error-correcting parser for context-free languages,” SIAM Journal of Computing, no. 4, pp. 305–312, 1972.CrossRefMathSciNetGoogle Scholar
  2. 2.
    H. Asada and M. Brady, “The curvature primal sketch,” in Proc. Workshop on Computer Vision (M. Caudill and C. Butler, eds.), (Annapolis, MD), pp. 609–618, 1984.Google Scholar
  3. 3.
    D. Ballard, “Strip trees: a hierarchical representation for map features,” in Proc. of the 1979 IEEE Computer Society Conference on Pattern Recognition and Image Processing, (New York, NY), pp. 278–285, IEEE, 1979.Google Scholar
  4. 4.
    D. Ballard, “Strip trees: a hierarchical representation for curves,” Communications of the ACM, vol. 24, pp. 310–321, 1981.CrossRefGoogle Scholar
  5. 5.
    E. Baum and D. Haussler, “What size net gives valid generalization?,” Neural Computation, vol. 1, no. 1, pp. 151–160, 1989.CrossRefGoogle Scholar
  6. 6.
    H. Bunke, “Hybrid pattern recognition methods,” in Syntactic and Structural Patten Recognition: Theory and Applications (H. Bunke and A. Sanfeliu, eds.), ch. 11, pp. 307–347, World Scientific, 1990.Google Scholar
  7. 7.
    Clarke, F., Ekeland, I.: Nonlinear oscillations and boundary-value problems for Ha-miltonian systems. Arch. Rat. Mech. Anal. 78 (1982) 315–333MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Clarke, F., Ekeland, I.: Solutions p’eriodiques, du période donnée, des équations hamiltoniennes. Note CRAS Paris 287 (1978) 1013–1015MATHMathSciNetGoogle Scholar
  9. 9.
    M. Diligenti, M. Gori, M. Maggini, and E. Martinelli, “Graphical pattern recognition,” in Proceedings of ICAPR98, 1998.Google Scholar
  10. 10.
    C. Dyer, A. Rosenfeld, and H. Samet, “Region representation: Boundary codes from quadtrees,” Communications of the ACM, vol. 23, pp. 171–179, 1980.MATHCrossRefGoogle Scholar
  11. 11.
    J. Fodor and Z. Pylyshyn, “Connectionism and cognitive architecture: A critical analysis,” Connections and Symbols, pp. 3–72, 1989. A Cognition Special Issue.Google Scholar
  12. 12.
    P. Frasconi, M. Gori, and A. Sperduti, “A general framework for adaptive processing of data structures,” IEEE Transactions on Neural Networks, vol. 9, pp. 768–786, September 1998.Google Scholar
  13. 13.
    P. Frasconi, M. Gori, and A. Sperduti, ‘Integration of graphical-based rules with adaptive learning of structured information,” in Hybrid Neural Systems, Springer Verlag, R. Sun and S. Wermeter Eds, March 2000Google Scholar
  14. 14.
    P. Frasconi, M. Gori, S. Marinai, J. Sheng, G. Soda, and A. Sperduti, “Logo recognition by recursive neural networks,” in Proceedings of GREC97, pp. 144–151, 1998.Google Scholar
  15. 15.
    H. Freeman, “On the encoding of arbitrary geometric configurations,” IRE Trans., vol. EC-10, pp. 260–268, 1961.MathSciNetGoogle Scholar
  16. 16.
    K. Fu, Syntactic pattern recognition. Englewood Cliffs, NJ: Prentice-Hall, 1982.MATHGoogle Scholar
  17. 17.
    C. Goller and M. Gori, “Feature extraction and learning vector quantization for data structure,” in Proceedings of SOCO’99, (Genoa (Italy)), June 1999.Google Scholar
  18. 18.
    M. Gori and F. Scarselli, “Are multilayer perceptrons adequate for pattern recognition and verification?”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 10, pp. 1121–1132, 1998.CrossRefGoogle Scholar
  19. 19.
    G. Hunter, Efficient Computation and Data Structures for Graphics. PhD thesis, Dept. of Electrical Engineering and Computer Science, Princeton University, Princeton, NJ, 1978.Google Scholar
  20. 20.
    G. Hunter and K. Steiglitz, “Operations on images using quadtrees,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, pp. 145–153, 1979.CrossRefGoogle Scholar
  21. 21.
    S. Lu and K. Fu, “Stochastic error-correction syntax analysis for recognition of noisy patterns,” IEEE Transactions on Computers, no. 26, pp. 1268–1276, 1977.Google Scholar
  22. 22.
    D. Meagher, “Geometric modelling using octree encoding,” Computer Graphics and Image Processing, pp. 129–147, 1982.Google Scholar
  23. 23.
    Michalek, R., Tarantello, G.: Subharmonic solutions with prescribed minimal period for nonautonomous Hamiltonian systems. J. Diff. Eq. 72 (1988) 28–55MATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    M. Minsky and S. Papert, Perceptrons-Expanded Edition. Cambridge: MIT Press, 1988.Google Scholar
  25. 25.
    D. Rumelhart, J. McClelland, and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge: MIT Press, 1986.Google Scholar
  26. 26.
    P. Salembier and L. Garrido, “Binary partition tree as an efficient representation for filtering, segmentation and information retrieval,” in IEEE Int. Conference on Image Processing, ICIP’98, vol. 2, (Los Alamitos, CA), pp. 252–256, IEEE Comp. Soc. Press, 1998.Google Scholar
  27. 27.
    H. Samet, “Spatial data structures,” in Modern Database Systems: The Object Model, Interoperability, and Beyond (W. Kim, ed.), pp. 361–385, Reading, MA: Addison Wesley/ACM Press, 1995.Google Scholar
  28. 28.
    A. Sperduti and A. Starita, “Supervised neural networks for the classification of structures” IEEE Trans. on Neural Networks, vol. 8, no. 3, pp. 714–735, 1997.CrossRefGoogle Scholar
  29. 29.
    W.-H. Tsai, “Combining statistical and structural methods,” in Syntactic and Structural Patten Recognition: Theory and Applications (H. Bunke and A. Sanfeliu, eds.), ch. 12, pp. 349–366, World Scientific, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Giovanni Adorni
    • 1
  • Stefano Cagnoni
    • 1
  • Marco Gori
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá di ParmaItaly
  2. 2.Dipartimento di Ingegneria dell’InformazioneUniversitá di SienaSienaItaly

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