Structural Data Recognition with Graph Model Boosting

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) only a single model is used to capture structural variation and 2) naive classifiers are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The main contribution of this paper is a novel approach to structural data recognition: graph model boosting. We construct a large number of graph models and train a strong classifier using the models in a boosting framework. Comprehensive structural variation is captured with a large number of graph models. Consequently, we can perform structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments using IAM graph database repository show that the proposed method achieves impressive results and outperforms existing methods.