Neural Style Representations and the Large-Scale Classification of Artistic Style
This addresses the challenge for art historians and computer vision researchers in automating style classification, though it appears incremental as it builds on existing neural-style algorithms.
The paper tackled the problem of classifying artistic style in paintings by investigating the effectiveness of a 'neural-style' representation derived from convolutional neural networks, resulting in a method for large-scale classification.
The artistic style of a painting is a subtle aesthetic judgment used by art historians for grouping and classifying artwork. The recently introduced `neural-style' algorithm substantially succeeds in merging the perceived artistic style of one image or set of images with the perceived content of another. In light of this and other recent developments in image analysis via convolutional neural networks, we investigate the effectiveness of a `neural-style' representation for classifying the artistic style of paintings.