Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks

TL;DR

This paper introduces a deep learning approach to identify characteristic landmarks on ancient Roman coins, enabling both region discovery aligned with expert annotations and improved coin recognition accuracy.

Contribution

The paper presents a novel CNN-based method for discovering class-specific regions on coins and a hierarchical classification framework leveraging the structure of Roman coins.

Findings

Successfully identified characteristic regions consistent with experts

Enhanced coin recognition accuracy using the proposed framework

Created a new annotated Roman coin dataset

Abstract

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.