Classifying Ancient Coins by Local Feature Matching and Pairwise Geometric Consistency Evaluation

Classification of ancient coins is a substantial part of numismatic research which needs a large amount of expert knowledge due to the high number of classes to be considered. In this paper we propose an automatic image-based classification method for ancient coins to support this time-consuming and difficult process. We demonstrate that previously proposed learning-based methods suffer from the practical conditions of this problem: a high number of classes, limited number of training samples per class and complex intra-class variations. As a solution we propose a similarity metric based on feature correspondence which is designed to be robust against the possible intra-class coin variations like degraded parts, non-rigid deformations and illumination-induced appearance changes. The similarity metric is used in an exemplar-based ancient coin classification scheme which shows to outperform previously proposed methods for ancient coin recognition. Experiments are conducted on a dataset of 60 Roman Republican coin classes where the presented method achieves classification rates ranging from 72.7% for the case of one training sample per class up to 97.2% when nine training samples per class are used.