South Australian researchers at Flinders University are analyzing the evolution of rock art via machine learning.
The team studied images of art collected during surveys of the Arnhem Land region using previously trained and published convolutional neural network models and dataset combinations each designed for object classification.
The Flinders investigators used transfer learning to deploy these networks on the dataset without retraining, and analyzed the models' response or activation on a rock art dataset.
Flinders' Daryl Wesley said the computer observed over 1,000 different types of objects, and learned to differentiate them by looking at photos.
Flinders' Ian Moffat said transfer learning removed a significant amount of human bias from the analysis, and an especially exciting aspect of this research is that "it is replicating the results of other studies that have used a more traditional approach."
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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