Neural Style Representations and the Large-Scale Classification of Artistic Style

Any observer can sense the artistic style of painting, even if it takes training to articulate it. To an art historian, the artistic style is the primary means of classifying the painting [10]. However, artistic style is not well defined, and may be loosely described as ".. a distinctive manner which permits the grouping of works into related categories" [1]. Algorithmically determining the artistic style of an artwork is a challenging problem which may include analysis of features such as the painting's color, its texture, and its subject matter, or none of those at all. Detecting the style of a digitized image of a painting poses additional challenges raised by the digitization process, which itself has consequences that may affect the ability of a machine to correctly detect artistic style; for instance, textures may be affected by the resolution of the digitization. Despite these challenges, intelligent systems for detecting artistic style would be useful for identification and retrieval of images of a similar style. In this paper we investigate several methods based on recent advances in convolutional neural networks for large-scale determination of artistic style. In particular, we adapt the neural-style algorithm introduced in [2] for large-scale style classification, showing performance that is competitive with other deep convolutional neural network based approaches. 1 Figure 1: Original image on the left, after application of the'neural-style' algorithm (style image'Starry Night', by Van Gogh) on the right.