1. Discerning the painter's hand: machine learning on surface topography
- Author
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Ji, F., McMaster, M. S., Schwab, S., Singh, G., Smith, L. N., Adhikari, S., O'Dwyer, M., Sayed, F., Ingrisano, A., Yoder, D., Bolman, E. S., Martin, I. T., Hinczewski, M., and Singer, K. D.
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a confocal optical profilometer to produce surface data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60 to 96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, as small as twice a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice., Comment: main text: 24 pages, 6 figures; SI: 6 pages, 4 figures
- Published
- 2021