Toward Automated Discovery of Artistic Influence

TL;DR

This paper explores automated methods for discovering artistic influence by comparing different classification models and features, and visualizing artist similarities to aid art historical research.

Contribution

It introduces the first general approach to computer-automated influence detection between artists, including a comparative study of classification methods and similarity measures.

Findings

Discriminative models outperform generative models in style classification.

Semantic features yield better style classification accuracy.

Artist similarity measures can effectively visualize influence relationships.

Abstract

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works