Manuel Domínguez-Rodrigo, Juliet Brophy, Gregory J. Mathews, Marcos Pizarro-Monzo, Enrique Baquedano
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African bovid tribe classification using transfer learning and computer vision
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.
期刊介绍:
Published on behalf of the New York Academy of Sciences, Annals of the New York Academy of Sciences provides multidisciplinary perspectives on research of current scientific interest with far-reaching implications for the wider scientific community and society at large. Each special issue assembles the best thinking of key contributors to a field of investigation at a time when emerging developments offer the promise of new insight. Individually themed, Annals special issues stimulate new ways to think about science by providing a neutral forum for discourse—within and across many institutions and fields.