Graph matching: Fast candidate elimination using machine learning techniques

Abstract

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 classification rules. Experimental results are reported demonstrating that efficient candidate elimination can be achieved by the proposed procedure. © Springer-Verlag Berlin Heidelberg 2000.