id
	16367
Url
	https://chloe.cnr.it/s/BiDiAr/item/16367
Resource template
	Academic Article
Resource class
	bibo:AcademicArticle
Title
	Learning feature representation of Iberian ceramics with automatic classification models
Creator
	Navarro, Pablo
	Cintas, Celia
	Lucena, Manuel
	Fuertes, José Manuel
	Delrieux, Claudio
	Molinos, Manuel
Date
	2021
Language
	eng
Abstract
	In Cultural Heritage inquiries, a common requirement is to establish time-based trends between archaeological artifacts belonging to different periods of a given culture, enabling among other things to determine chronological inferences with higher accuracy and precision. Among these, pottery vessels are significantly useful, given their relative abundance in most archaeological sites. However, this very abundance makes difficult and complex an accurate representation, since no two of these vessels are identical, and therefore classification criteria must be justified and applied. For this purpose, we propose the use of deep learning architectures to extract automatically learned features without prior knowledge or engineered features. By means of transfer learning, we retrained a Residual Neural Network with a binary image database of Iberian wheel-made pottery vessels’ profiles. These vessels pertain to archaeological sites located in the upper valley of the Guadalquivir River (Spain). The resulting model can provide an accurate feature representation space, which can automatically classify profile images, achieving a mean accuracy of 0.96 with an f-measure of 0.96. This accuracy is remarkably higher than other state-of-the-art machine learning approaches, where several feature extraction techniques were applied together with multiple classifier models. These results provide novel strategies to current research in automatic feature representation and classification of different objects of study within the Archaeology domain.
Is Part Of
	Journal of Cultural Heritage
Doi
	https://doi.org/10.1016/j.culher.2021.01.003
Issn
	1296-2074
Pages
	65-73
Uri
	https://www.sciencedirect.com/science/article/pii/S1296207421000042
Volume
	48
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/7L2DMNH9item-list

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