Name
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 frame-work. 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MetroArchaeo43810.2018.13605 | 2018 Metrology for Archaeology and Cultural Heritage (MetroArchaeo) |
Keywords | Field | DocType |
ancient coin classification,graph transduction games,coin type recognition | Training set,Graph,Computer science,Artificial intelligence,Final Labeling,Contextual image classification,Machine learning | Journal |
Volume | ISBN | Citations |
abs/1810.01091 | 978-1-5386-5277-0 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sinem Aslan | 1 | 16 | 5.19 |
| Sebastiano Vascon | 2 | 35 | 6.04 |
| Marcello Pelillo | 3 | 1888 | 150.33 |