  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.
