Ancient Coin Classification Using Graph Transduction Games
This work addresses the time-consuming task of ancient coin recognition for numismatists, presenting an incremental improvement over existing methods on a specific dataset.
The paper tackles the problem of automating ancient coin classification by proposing a visual classification framework using Graph Transduction Games (GTG), achieving accuracies of 73.6% and 87.3% with one and two training images per class, respectively, on a dataset of Roman coins.
Recognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. Our goal in this work is automatizing this time consuming and demanding task by a visual classification framework. Specifically, we propose to model ancient coin image classification using Graph Transduction Games (GTG). GTG casts the classification problem as a non-cooperative game where the players (the coin images) decide their strategies (class labels) according to the choices made by the others, which results with a global consensus at the final labeling. Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins. We demonstrate that our approach outperforms the literature work on the same dataset with the classification accuracy of 73.6% and 87.3% when there are one and two images per class in the training set, respectively.