Special Section on Image Processing for Cultural Heritage

Discovering characteristic landmarks on ancient coins using convolutional networks

[+] Author Affiliations
Jongpil Kim, Vladimir Pavlovic

Rutgers, The State University of New Jersey, Department of Computer Science, 110 Frelinghuysen Road, Piscataway, New Jersey 08854, United States

J. Electron. Imaging. 26(1), 011018 (Dec 28, 2016). doi:10.1117/1.JEI.26.1.011018
History: Received July 1, 2016; Accepted November 29, 2016
Text Size: A A A

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.

Figures in this Article
© 2016 SPIE and IS&T

Citation

Jongpil Kim and Vladimir Pavlovic
"Discovering characteristic landmarks on ancient coins using convolutional networks", J. Electron. Imaging. 26(1), 011018 (Dec 28, 2016). ; http://dx.doi.org/10.1117/1.JEI.26.1.011018


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.