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Abstract

In structural pattern recognition it is often required to match an unknown sample against a database of candidate patterns in order to find the most similar prototype. If the patterns are represented using graphs, the sample’s graph is matched against a database of model graphs and the pattern recognition problem is turned into a graph matching problem. Graph matching is a powerful yet computationally expensive procedure. If the unknown sample is matched against a whole database of prototypes, the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor an approach based on machine learning techniques is proposed in this paper. The graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates. Experimental results are reported demonstrating the efficiency of the proposed filtering procedure. The work presented in this paper extends previous studies from the case of graph-isomorphism to the problem of subgraph-isomorphism.

Keywords

Decision Tree Graph Match Graph Isomorphism Subgraph Isomorphism Graph Database 
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.
    Llados, J., Marti, E., Villanueva, J.: Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23–10, pp. 1137–1143 (2001)Google Scholar
  2. 2.
    Torsella, A., Hancock, E.: Learning stuctural variations in shock trees. In: Proc. of the Joint IAPR International Workshops SSPR and SPR, pp. 113–122 (2002)Google Scholar
  3. 3.
    Luo, B., Hancock, E.: Structural graph matching using the em algorithm and singular value decomposition. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 23–10, pp. 1120–1136 (2001)Google Scholar
  4. 4.
    Lumini, A., Maio, D., Maltoni, D.: Inexact graph matching for fingerprint classification. Machine Graphics and Vision. Special Issue on Graph Transformations in Pattern Generation and CAD 8, 231–248 (1999)Google Scholar
  5. 5.
    Chen, H., Lin, H., Liu, T.: Multi-object tracking using dynamical graph matching. In: Proc. of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 210–217 (2001)Google Scholar
  6. 6.
    Guigno, R., Sasha, D.: Graphgrep: A fast and universal method for querying graphs. In: Proceeding of the International Conference in Pattern Recognition (ICPR), pp. 112–115 (2002)Google Scholar
  7. 7.
    Wang, J.: l., Sasha, D., Guigno, R.: Algorithmics and applications of tree and graph searching. In: Proceeding of the ACM Symposium on Principles of Database Systems, PODS (2002)Google Scholar
  8. 8.
    Messmer, B., Bunke, H.: A new algorithm for error–tolerant subgraph isomorphism detection. In: IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, pp. 493–505 (1998)Google Scholar
  9. 9.
    Shapiro, L., Haralick, R.: Organization of relational models for scene analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 3, 595–602 (1982)CrossRefGoogle Scholar
  10. 10.
    Sengupta, K., Boyer, K.: Organizing large structural modelbases. In: IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17 (1995)Google Scholar
  11. 11.
    Irniger, C., Bunke, H.: Graph matching: Filtering large databases of graphs using decision trees. In: Jolion, J.M., Kropatsch, W., Vento, M. (eds.) Graph-based Representations in Pattern Recognition, Cuen, pp. 239–249 (2001)Google Scholar
  12. 12.
    Irniger, C., Bunke, H.: Theoretical analysis and experimental comparison of graph matching algorithms for database filtering. In: Hancock, E., Vento, M. (eds.) Graphbased Representations in Pattern Recognition, pp. 118–129. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Irniger, C., Bunke, H.: Graph database filtering using decision trees. In: Proceedings of the International Conference in Pattern Recognition, ICPR (2004)Google Scholar
  14. 14.
    Quinlan, J.: C4.5: Programs for Machine Learning. In: Document Analysis Systems II, Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar
  15. 15.
    Ullmann, J.: An algorithm for subgraph isomorphism. In: JACM, vol. 23, pp. 31–42 (1976)Google Scholar
  16. 16.
    Cordella, L., Foggia, P., Sansone, C., Vento, M.: An improved algorithm for matching large graphs. In: Jolion, J.M., Kropatsch, W., Vento, M. (eds.) Graph-based Representations in Pattern Recognition, Cuen, pp. 149–159 (2001)Google Scholar
  17. 17.
    McKay, B.: Practical graph isomorphism. In: Congressus Numerantium, vol. 30, pp. 45–87 (1981)Google Scholar
  18. 18.
    Foggia, P., Sansone, C., Vento, M.: A database of graphs for isomorphism and subgraph isomorphism. In: Jolion, J.M., Kropatsch, W., Vento, M. (eds.) Graph-based Representations in Pattern Recognition, Cuen, pp. 176–188 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Christophe Irniger
    • 1
  • Horst Bunke
    • 1
  1. 1.Department of Computer ScienceUniversity of BernBernSwitzerland

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