Abstract
How does the machine classify styles in art? And how does it relate to art
historians's methods for analyzing style? Several studies have shown the
ability of the machine to learn and predict style categories, 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 77K 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 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.
Description
[1801.07729] The Shape of Art History in the Eyes of the Machine
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