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An Image-Based Class Retrieval System for Roman Republican Coins.

Hafeez Anwar1,2, Serwah Sabetghadam3, Peter Bell1

  • 1Interdisciplinary Center for Digital Humanities and Social Sciences, Friedrich-Alexander University, 91052 Erlangen, Germany.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

We developed an image-based system for retrieving ancient Roman coins, achieving 99% accuracy. This system efficiently classifies coins and retrieves numismatic information, aiding archaeological studies and online platforms.

Keywords:
image classificationimage entropyimage processing

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Area of Science:

  • Archaeological Science
  • Computer Vision
  • Digital Humanities

Background:

  • Accurate classification and information retrieval of ancient Roman Republican coins are crucial for numismatics, museums, and online auctions.
  • Existing methods face challenges with large datasets, requiring impractical brute-force matching and potentially overwhelming users with excessive information.
  • A need exists for an efficient system that balances classification accuracy with computational and user interface convenience.

Purpose of the Study:

  • To propose and evaluate an image-based class retrieval system for ancient Roman Republican coins.
  • To optimize the system for maximum classification accuracy while minimizing computational complexity and user interface overload.
  • To facilitate efficient numismatic study and archaeological applications through user-friendly coin classification and information retrieval.

Main Methods:

  • Developed an image-based retrieval system employing a user-friendly graphical user interface (GUI).
  • Implemented a matching algorithm that compares query coin images against a database of exemplar coin class images.
  • Optimized the matching process by incrementally varying the number of matches per class and the search space for coin classes to identify optimal parameters for accuracy and efficiency.

Main Results:

  • Achieved a classification accuracy of 99% on the current dataset.
  • Determined that considering five matches per class and the top ten retrieved classes yields maximum classification accuracy.
  • Significantly reduced computational complexity by matching query images with approximately half the exemplar images per class.

Conclusions:

  • The proposed image-based retrieval system offers a highly accurate and efficient solution for classifying ancient Roman Republican coins.
  • The optimization strategy effectively balances classification performance with reduced computational load and improved user experience.
  • This system holds significant potential for applications in numismatics, museum collections, and online marketplaces for ancient artifacts.