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An Image-Based Class Retrieval System for Roman Republican Coins
We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but al...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517372/ https://www.ncbi.nlm.nih.gov/pubmed/33286570 http://dx.doi.org/10.3390/e22080799 |
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author | Anwar, Hafeez Sabetghadam, Serwah Bell, Peter |
author_facet | Anwar, Hafeez Sabetghadam, Serwah Bell, Peter |
author_sort | Anwar, Hafeez |
collection | PubMed |
description | We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes. |
format | Online Article Text |
id | pubmed-7517372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75173722020-11-09 An Image-Based Class Retrieval System for Roman Republican Coins Anwar, Hafeez Sabetghadam, Serwah Bell, Peter Entropy (Basel) Article We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes. MDPI 2020-07-22 /pmc/articles/PMC7517372/ /pubmed/33286570 http://dx.doi.org/10.3390/e22080799 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Anwar, Hafeez Sabetghadam, Serwah Bell, Peter An Image-Based Class Retrieval System for Roman Republican Coins |
title | An Image-Based Class Retrieval System for Roman Republican Coins |
title_full | An Image-Based Class Retrieval System for Roman Republican Coins |
title_fullStr | An Image-Based Class Retrieval System for Roman Republican Coins |
title_full_unstemmed | An Image-Based Class Retrieval System for Roman Republican Coins |
title_short | An Image-Based Class Retrieval System for Roman Republican Coins |
title_sort | image-based class retrieval system for roman republican coins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517372/ https://www.ncbi.nlm.nih.gov/pubmed/33286570 http://dx.doi.org/10.3390/e22080799 |
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