Representative Graph Generation for Graph-Based Character Recognition

Abstract

〈Summary〉 In graph-based pattern recognition, representative graph influences the performances of recognition and clustering. In this paper, we propose a learning method for generating a representative graph of a set of graphs by constructing graph unions with merging corresponding vertices and edges. Those corresponding vertices and edges are obtained using common vertices of a set. The proposed method includes extracting common vertices and correspondences of vertices. To show the validly of the proposed method, we applied the proposed method to pattern recognition problems with character graph database and graphs obtained from decorative character images.