African bovid tribe classification using transfer learning and computer vision.

Autor: Domínguez-Rodrigo M; Institute of Evolution in Africa (IDEA), University of Alcalá de Henares, Madrid, Spain.; Area of Prehistory, Department of History and Philosophy, University of Alcalá de Henares, Alcalá de Henares, Spain.; Department of Anthropology, Rice University, Houston, Texas, USA., Brophy J; Department of Geography and Anthropology, Louisiana State University, Baton Rouge, Louisiana, USA.; Centre for the Exploration of the Deep Human Journey, University of the Witwatersrand, Johannesburg, South Africa., Mathews GJ; Department of Mathematics and Statistics, Loyola University, University of Chicago, Chicago, Illinois, USA., Pizarro-Monzo M; Institute of Evolution in Africa (IDEA), University of Alcalá de Henares, Madrid, Spain.; Institut Català de Paleoecologia Humana i Evolució Social (IPHES), Universitat Rovira i Virgili, Tarragona, Spain., Baquedano E; Institute of Evolution in Africa (IDEA), University of Alcalá de Henares, Madrid, Spain.
Jazyk: angličtina
Zdroj: Annals of the New York Academy of Sciences [Ann N Y Acad Sci] 2023 Dec; Vol. 1530 (1), pp. 152-160. Date of Electronic Publication: 2023 Oct 07.
DOI: 10.1111/nyas.15067
Abstrakt: Objective analytical identification methods are still a minority in the praxis of paleobiological sciences. Subjective interpretation of fossils and their modifications remains a nonreplicable expert endeavor. Identification of African bovids is a crucial element in the reconstruction of paleo-landscapes, ungulate paleoecology, and, eventually, hominin adaptation and ecosystemic reconstruction. Recent analytical efforts drawing on Fourier functional analysis and discrimination methods applied to occlusal surfaces of teeth have provided a highly accurate framework to correctly classify African bovid tribes and taxa. Artificial intelligence tools, like computer vision, have also shown their potential to be objectively more accurate in the identification of taphonomic agency than human experts. For this reason, here we implement some of the most successful computer vision methods, using transfer learning and ensemble analysis, to classify bidimensional images of African bovid teeth and show that 92% of the large testing set of images of African bovid tribes analyzed could be correctly classified. This brings an objective tool to paleoecological interpretation, where bovid identification and paleoecological interpretation can be more confidently carried out.
(© 2023 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of The New York Academy of Sciences.)
Databáze: MEDLINE
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