Rulegraphs for graph matching in pattern recognition

In Pattern Recognition, the Graph Matching problem involves the matching of a sample graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present Rulegraphs, a new method that combines the Graph Matching approach with Rule-Based approaches from Machine Learning. They reduce the cardinality of the (NP-Complete) Graph Matching problem by replacing model parts and their relations by rules. We show how rulegraphs, combined with techniques for checking feature label-compatibilities, not only reduce the search space but also improve the uniqueness of the matching process.