Discovering artistic influences of painters from expressionism, impressionism, and surrealism art movements using convolutional neural network

Abstract

This paper explores the use of Convolutional Neural Network (CNN) to discover the artistic influences of painters from three major art movements - Expressionism, Impressionism, and Surrealism. Since artistic influence is a subjective inference by art experts, this paper offers a quantitative solution to this seemingly subjective problem. We propose two solutions. The first solution involves multi-class, artist-based classification using several CNN models including Alexnet, VGG16, and Resnet18. The idea is, if various models consistently misclassify certain artworks to another artist, then the models consider the artworks of the two to be similar. With that, artistic influence is drawn from consistent false positives in the confusion matrices. The second approach is retrieving visually similar paintings in a large-scale data set. Like the first approach, if artworks of two artists are deemed to be visually similar by a computer, then artistic influence can be drawn between the two. The retrieval process in finding similarities in paintings involves extracting features using a CNN, applying a dimension reduction technique, then computing the Euclidean distances between the features and returning the nearest neighbor. With a long list of artists with possible influences, a graph network is created to map the interactions on a wider point of view. The proposed methods were able to validate several historical claims of artistic influences and provide new possible artistic influences that have never been considered before by art historians.