Exploring the influence of scale on artist attribution

TL;DR

This paper investigates how the resolution scale of digital artwork images affects artist attribution accuracy, demonstrating that multi-scale CNNs improve forensic analysis by leveraging fine details.

Contribution

It introduces a multi-scale CNN approach for artist attribution, combining existing methods to analyze artworks at various resolutions, highlighting the benefits of fine-scale analysis.

Findings

Finer scales generally improve attribution accuracy.

For some artists, coarser scales are more effective.

Multi-scale CNNs expand computational art forensics.

Abstract

Previous work has shown that the artist of an artwork can be identified by use of computational methods that analyse digital images. However, the digitised artworks are often investigated at a coarse scale discarding many of the important details that may define an artist's style. In recent years high resolution images of artworks have become available, which, combined with increased processing power and new computational techniques, allow us to analyse digital images of artworks at a very fine scale. In this work we train and evaluate a Convolutional Neural Network (CNN) on the task of artist attribution using artwork images of varying resolutions. To this end, we combine two existing methods to enable the application of high resolution images to CNNs. By comparing the attribution performances obtained at different scales, we find that in most cases finer scales are beneficial to the attribution performance, whereas for a minority of the artists, coarser scales appear to be preferable. We conclude that artist attribution would benefit from a multi-scale CNN approach which vastly expands the possibilities for computational art forensics.