Discovering characteristic landmarks on ancient coins using convolutional networks

Jongpil Kim; Vladimir Pavlovic

doi:10.1117/1.JEI.26.1.011018

28 December 2016 Discovering characteristic landmarks on ancient coins using convolutional networks

Jongpil Kim, Vladimir Pavlovic

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 011018 (December 2016). https://doi.org/10.1117/1.JEI.26.1.011018

Abstract

We propose a method to find characteristic landmarks and recognize ancient Roman imperial coins using deep convolutional neural networks (CNNs) combined with expert-designed domain hierarchies. We first propose a framework to recognize Roman coins that exploits the hierarchical knowledge structure embedded in the coin domain, which we combine with the CNN-based category classifiers. We next formulate an optimization problem to discover class-specific salient coin regions. Analysis of discovered salient regions confirms that they are largely consistent with human expert annotations. Experimental results show that the proposed framework is able to effectively recognize ancient Roman coins as well as successfully identify landmarks on the coins. For this research, we have collected a Roman coin dataset where all coins are annotated and consist of obverse (head) and reverse (tail) images.

Citation Download Citation

Jongpil Kim and Vladimir Pavlovic "Discovering characteristic landmarks on ancient coins using convolutional networks," Journal of Electronic Imaging 26(1), 011018 (28 December 2016). https://doi.org/10.1117/1.JEI.26.1.011018

Received: 1 July 2016; Accepted: 29 November 2016; Published: 28 December 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available