Analyzing Notions of Artistic Style Using Computer Vision Techniques

Computer vision has made significant strides in the area of artistic style transfer, and a few attempts have been made to extract and define the style signature of various artists. However, most of these endeavors have been limited by treating a creative task such as painting and critiquing style as a traditional machine learning problem. In this study, we try to shift the viewpoint from machine learning trying to solve an art problem, to one where the art world is using computer vision techniques to fit its purpose. This subtle difference is extremely important because it allows us to build notions of style in a bottom up fashion, rooted in the domain knowledge pertaining to artistic style.

This work aims to take first steps towards building an understanding of similarity in artistic style with the intention of critiquing and valuing art. I begin by constructing a dataset of approximately 14,000 high-resolution paintings, which are part of the Google Art Project, available under the Wikimedia Commons License. I explore the possibility of neural style transfer features being good measures of style similarity, and proceed to develop computationally useful style features for scene composition, color palette, brush strokes, and contours. I conduct a domain-specific study wtih experts to validate the importance of each of the style features. I then present a novel way to normalize and weight these features based on the external study and develop a cumulative measure of artistic style similarity. Finally, I validate the results qualitatively on historically accepted examples in the art community, and quantitatively via a second domain study with experts.