The Shape of Art History in the Eyes of the Machine

Authors

  • Ahmed Elgammal Rutgers University
  • Bingchen Liu Rutgers University
  • Diana Kim Rutgers University
  • Mohamed Elhoseiny Facebook AI Research
  • Marian Mazzone College of Charleston

Keywords:

computational art history, applications of AI, Art and AI

Abstract

How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies showed the ability of the machine to learn and predict styles, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 67K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are a few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich Wölfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations.

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Published

2018-04-26

How to Cite

Elgammal, A., Liu, B., Kim, D., Elhoseiny, M., & Mazzone, M. (2018). The Shape of Art History in the Eyes of the Machine. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11894

Issue

Section

Main Track: Machine Learning Applications