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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...

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Autores principales: Anwar, Hafeez, Sabetghadam, Serwah, Bell, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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