Learning feature representation of Iberian ceramics with automatic classification models

Title

Learning feature representation of Iberian ceramics with automatic classification models

Creator(s)

Navarro, Pablo

Cintas, Celia

Lucena, Manuel

Fuertes, José Manuel

Delrieux, Claudio

Molinos, Manuel

Date

2021

Is Part Of

Journal of Cultural Heritage

Volume

48

Pages

65-73

Language

eng

ISSN

1296-2074

DOI

https://doi.org/10.1016/j.culher.2021.01.003

URI

https://www.sciencedirect.com/science/article/pii/S1296207421000042

Zotero BiDiAr

https://www.zotero.org/groups/5293298/bidiar/items/7L2DMNH9item-list

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.