Graph matching is an important class of methods in pattern recognition. Typically, a graph representing an unknown pattern is matched with a database of models. If the database of model graphs is large, an additional factor in induced into the overall complexity of the matching process. Various techniques for reducing the influence of this additional factor have been described in the literature. In this paper we propose to extract simple features from a graph and use them to eliminate candidate graphs from the database. The most powerful set of features and a decision tree useful for candidate elimination are found by means of the C4.5 algorithm, which was originally proposed for inductive learning of classication rules. Experimental results are reported demonstrating that effcient candidate elimination can be achieved by the proposed procedure.
8th International Workshop on Structural and Syntactic Pattern Recognition, 3rd International Workshop on Statistical Techniques in Pattern Recognition
Publication classification
E1.1 Full written paper - refereed
Copyright notice
2000, Springer-Verlag Berlin Heidelberg
Editor/Contributor(s)
F Ferri, J Inesta, A Amin, P Pudil
Title of proceedings
Advances in Pattern Recognition : Joint IAPR International Workshops SSPR 2000 and SPR 2000, Alicante, Spain, August 30 – September 1, 2000 proceedings