Prototype Learning with Attributed Relational Graphs

  • Pasquale Foggia
  • Roberto Genna
  • Mario Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

An algorithm for learning structural patterns given in terms of Attributed Relational Graphs (ARG’s) is presented. The algorithm, based on inductive learning methodologies, produces general and coherent prototypes in terms of Generalized Attributed Relational Graphs (GARG’s), which can be easily interpreted and manipulated. The learning process is defined in terms of inference operations especially devised for ARG’s, as graph generalization and graph specialization, making so possible the reduction of both the computational cost and the memory requirement of the learning process. Experimental results are presented and discussed with reference to a structural method for recognizing characters extracted from ETL database.

Keywords

Graph Match Heuristic Function Edge Attribute Graph Match Algorithm Inference Operation 
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.

References

  1. 1.
    T. Pavlidis “Structural Pattern Recognition”, Springer, New York, 1977.Google Scholar
  2. 2.
    B. T. Messmer and H. Bunke, “A new algorithm for error-tolerant subgraph isomorphism detection”, IEEE Trans. on PAMI, vol. 20, n. 5, pp. 493–504, May 1998.Google Scholar
  3. 3.
    P. H. Winston, “Learning Structural Descriptions from Examples”, Tech. Report MAC-TR-76, Dep. of Electrical Engineering and Computer Science-MIT, 1970Google Scholar
  4. 4.
    L. P. Cordella, P. Foggia, R. Genna and M. Vento, “Prototyping Structural Descriptions: an Inductive Learning Approach”, Advances in Pattern Recognition, Lecture Notes in Computer Science, n. 1451, pp. 339–348, Springer-Verlag, 1998.CrossRefGoogle Scholar
  5. 5.
    R. S. Michalski, “Pattern recognition as rule-guided inductive inference”, IEEE Trans. on PAMI, vol. 2, n. 4, pp. 349–361, July 1980.MATHGoogle Scholar
  6. 6.
    N. Lavrac, S. Dzeroski, “Inductive Logic Programming: Techniques and Applications”, Ellis Horwood, 1994.Google Scholar
  7. 7.
    A. Pearce, T. Caelly, and W. F. Bischof, “Rulegraphs for graph matching in pattern recognition”, Pattern Recognition, vol. 27, n. 9, pp. 1231–1247, 1994.CrossRefGoogle Scholar
  8. 8.
    J. Hsu, S. Wang, “A machine learning approach for acquiring descriptive classification rules of shape contours”, Pattern Recognition, vol. 30, n. 2, pp. 245–252, 1997.CrossRefGoogle Scholar
  9. 9.
    J. R. Quinlan, “Learning Logical Definitions from Relations”, Machine Learning, vol. 5, n. 3, pp. 239–266, 1993.Google Scholar
  10. 10.
    L.P. Cordella, P. Foggia, C. Sansone, M. Vento, “Subgraph Transformations for the Inexact Matching of Attributed Relational Graphs”, Computing, vol. 12, pp. 43–52, 1998.MathSciNetGoogle Scholar
  11. 11.
    L.P. Cordella, P. Foggia, C. Sansone, M. Vento, “Performance Evaluation of the VF Graph Matching Algorithm”, Proc. 10th ICIAP, 1999, to appear.Google Scholar
  12. 12.
    ETL Character Database, Electrotechnical Laboratory-Japanese Technical Committe for OCR. Distributed during the 2nd ICDAR, 1993.Google Scholar
  13. 13.
    A. Chianese, L.P. Cordella, M. De Santo and M. Vento, “Decomposition of ribbon-like shapes”, Proc. 6th SCIA, Oulu, Finland, pp. 416–423, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Pasquale Foggia
    • 1
  • Roberto Genna
    • 1
  • Mario Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico II”NapoliItaly

Personalised recommendations