Ancient Coin Classification Using Graph Transduction Games

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

A Siamese transformer network for zero-shot ancient coin classification

Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin’s design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method’s errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking.Publisher PDFPeer reviewe

Dating The Earliest Coins Of Athens, Corinth And Aegina

Classic

Microstructure and chemical composition of Roman orichalcum coins emitted after the monetary reform of Augustus (23 B.C.)

A collection of ancient Roman orichalcum coins, i.e., a copper-zinc alloy, minted under the reigns from Caesar to Domitianus, have been characterised using scanning electron microscopy (SEM-EDS) and electron microprobe analysis (EMPA). We studied, for the first time, coins emitted by Romans after the reforms of Augustus (23 B.C.) and Nero (63-64 A.D). These coins, consisting of asses, sestertii, dupondii and semisses, were analysed using non- and invasive analyses, aiming to explore microstructure, corrosive process and to acquire quantitative chemical analysis. The results revealed that the coins are characterized by porous external layers, which are affected by dezincification and decuprification processes. As pictured by the X-ray maps, the elemental distribution of Cu and Zn shows patterns of depletion that in some cases penetrate in deep up to 1 mm. The composition of the un-corroded nucleus is a Cu-Zn alloy containing up to 30% of Zn, typical of coins produced via cementation process

Albert Mayr (1868-1924)

The Bavarian philologist Albert Mayr visited Malta during the autumn and winter months of 1897/98 and a second time in spring 1907. He is considered to have been a pioneer in many ways. His scientific approach to archaeology, at a time when the discipline was just getting beyond the point of myths and fables, enabled him to lay down solid foundations for various parts of Malta's prehistory and history.peer-reviewe