基于机器学习分类的古普塔弓箭手型钱币新数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Ishtiak Al Mamoon , Zakaria Shams Siam , Abdul Akhir Al Galib , Theophil Dango , Kalin Chakma , Pranto Dev , Rubyat Tasnuva Hasan , Muhammad E.H. Chowdhury
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引用次数: 0

摘要

在钱币学领域,对古钱币进行分类是一项艰巨的任务,尤其是对那些具有不同信息和文化遗产的古钱币。最近,机器学习算法在这类任务中取得了显著进步。然而,这些算法在很大程度上依赖于相关的数据集。本文介绍了一个新颖的古普塔弓箭手型古钱币图像数据集,该数据集是从经过验证的私人收藏和三家著名拍卖行征得同意后收集的。这些图像完全由古普塔弓箭型古钱币的真品标本组成。我们的目标是按照钱币研究的最高标准建立可靠的资源。这些钱币以其独特的弓箭手图案为特征,由于其稀缺性和设计的复杂性,给鉴定工作带来了巨大挑战。为了解决这个问题,我们通过目测和利用钱币文献中的见解相结合的方法,对每枚钱币进行注释,从而精心策划了一个数据集。这些钱币继承了古印度考古学的见解,研究这些钱币可以为古印度考古学提供见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel dataset of Gupta archer type coins for machine learning-based classification
In the field of numismatics, classifying ancient coins, especially those that have diverse information and cultural heritage is a difficult task. Machine learning algorithms have recently made remarkable advancements in these types of tasks. However, these algorithms largely rely on relevant datasets. This article presents a novel dataset of ancient Gupta archer-type coin images, collected from verified private collections and three popular auction houses with their permission. The images exclusively comprise authentic specimens of ancient Gupta archer-type coins. We aim to establish a reliable resource that adheres to the highest standards of numismatic research. These coins, characterized by their distinctive archer motifs, present a significant challenge in terms of identification due to their scarcity and the intricate nature of their design. To address this, we meticulously curated a dataset by annotating each coin through a combination of visual examination and leveraging insights from numismatic literatures. These coins inherit ancient Indian archaeological insights, and studying these coins could provide insights into ancient Indian archaeology.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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