How Convolutional Neural Networks Remember Art

Inspired by the successful performance of Convolutional Neural Networks (CNN) in automatically predicting complex image properties such as memorability, in this work we explore their transferability to the domain of art images. We employ a CNN model trained to predict memorability scores of natural images to explore the memorability of artworks belonging to different genres and styles. Our experiments show that nude painting and portrait are the most memorable genres, while landscape and marine painting are the least memorable. Regarding image style, we show that abstract styles tend to be more memorable than figurative. Additionally, on the subset of abstract images, we explore the relation between memorability and other features related to composition and color, as well as the sentiment evoked by the image. We show that there is no correlation between symmetry and memorability, however memorability positively correlates with the image's probability of evoking positive sentiment.