An analysis of paintings through multiple Convolutional Neural Networks for artistic in uence detection

Défense de mémoire de Monsieur Piotr Banach

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In the past few years, the quick success of Convolutional Neural Networks in
detecting and classifying images led to their adoption in a wide range of domains.
This includes the automated analysis of paintings, where new technologies are
expected to assist art historians and help maintain digital collections. Our work in
this thesis proposes a novel approach for detecting similarities and influences
across paintings based on information used for their classification by CNN. First, we
adapt three previously trained models to predict artistic genres, styles and provoked
emotions, showing improvements in their results when compared to some state of
the art solutions. Then we combine the models’ internal representations of paintings
to build a graph of similarities between artists across a dataset of 100.000 artworks.
The relations found using this approach are largely corroborated by available
documentation on well-known influences in art history and in some cases present
valuable new artistic insights.

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