Graph-Based multiple classifier systems a data level fusion approach

Authors:
Michel Neuhaus;Horst Bunke
Affiliations:
Department of Computer Science, University of Bern, Bern, Switzerland;Department of Computer Science, University of Bern, Bern, Switzerland
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

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Abstract

The combination of multiple classifiers has been successful in improving classification accuracy in many pattern recognition problems. For graph matching, the fusion of classifiers is normally restricted to the decision level. In this paper we propose a novel fusion method for graph patterns. Our method detects common parts in graphs in an error-tolerant way using graph edit distance and constructs graphs representing the common parts only. In experiments, we demonstrate on two datasets that the method is able to improve the classification of graphs.