info:eu-repo/semantics/article
Learning feature representation of Iberian ceramics with automatic classification models
Fecha
2021-02Registro en:
Navarro, Jose Pablo; Cintas, Celia; Lucena, Manuel; Fuertes, José Manuel; Delrieux, Claudio Augusto; et al.; Learning feature representation of Iberian ceramics with automatic classification models; Elsevier France-Editions Scientifiques Medicales Elsevier; Journal of Cultural Heritage; 48; 2-2021; 65-73
1296-2074
CONICET Digital
CONICET
Autor
Navarro, Jose Pablo
Cintas, Celia
Lucena, Manuel
Fuertes, José Manuel
Delrieux, Claudio Augusto
Molinos, Manuel
Resumen
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.