Ancient Coin Classification Using Graph Transduction Games

TL;DR

This paper introduces a novel graph transduction game approach for automating ancient coin classification, achieving higher accuracy than previous methods on a limited dataset of Roman coins.

Contribution

It applies Graph Transduction Games to coin classification, demonstrating improved accuracy and automating a traditionally expert-driven task.

Findings

Achieved 73.6% accuracy with one training image per class.

Achieved 87.3% accuracy with two training images per class.

Outperformed existing methods on the same dataset.

Abstract

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.