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.
Supported by the Swiss National Science Foundation NCCR program “Interactive Multimodal Information Management (IM)2” in the Individual Project “Multimedia Information Access and Content Protection”.
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Neuhaus, M., Bunke, H. (2005). Graph-Based Multiple Classifier Systems A Data Level Fusion Approach. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_59
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DOI: https://doi.org/10.1007/11553595_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
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