Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks
This work addresses the domain-specific problem of coin classification and landmark discovery for numismatics, representing an incremental advancement by applying existing CNN methods to a new task and dataset.
The paper tackles the problem of identifying characteristic landmarks on ancient Roman imperial coins by proposing a novel method using convolutional neural networks, which successfully finds class-specific regions consistent with human expert annotations and effectively recognizes coins with a new dataset.
In this paper, we propose a novel method to find characteristic landmarks on ancient Roman imperial coins using deep convolutional neural network models (CNNs). We formulate an optimization problem to discover class-specific regions while guaranteeing specific controlled loss of accuracy. Analysis on visualization of the discovered region confirms that not only can the proposed method successfully find a set of characteristic regions per class, but also the discovered region is consistent with human expert annotations. We also propose a new framework to recognize the Roman coins which exploits hierarchical structure of the ancient Roman coins using the state-of-the-art classification power of the CNNs adopted to a new task of coin classification. Experimental results show that the proposed framework is able to effectively recognize the ancient Roman coins. For this research, we have collected a new Roman coin dataset where all coins are annotated and consist of observe (head) and reverse (tail) images.