We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.
@cite_17: 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.
Unlike legends and portraits, the reverse motif is a discriminative visual cue that is less affected by wear and tear. Besides, a given reverse motif can be shared by coins of multiple classes. Therefore, the search space for the class of a given query coin image is aptly reduced by recognizing its reverse motif. This aspect makes the reverse motif-based coin classification coarse-grained that can further be refined by fine-grained classification methods @cite_17 .