PhotoStyle60: A Photographic Style Dataset for Photo Authorship Attribution and Photographic Style Transfer

Abstract

Photography, like painting, allows artists to express themselves through their unique style. In digital photography, this is achieved not only with the choice of the subject and the composition but also by means of post-processing operations. The automatic identification of a photographer from the style of a photo is a challenging task, for many reasons, including the lack of suitable datasets including photos taken by a diverse panel of photographers with a clear photographic style. In this paper we present PhotoStyle60, a new dataset including 5708 photographs from 60 professional and semi-professional photographers. Additionally, we selected a reduced version of the dataset, called PhotoStyle10 containing images from 10 clearly distinguishable experts. We designed the dataset to address two tasks in particular: photo authorship attribution and photographic style transfer. In the former, we conducted an extensive analysis of the dataset through several classification experiments. In the latter, we explored the potential of our dataset to transfer a photographer's style to images from the Five-K dataset. Additionally, we propose also a simple but effective multi-image style transfer method that uses multiple samples of the target style. A user study demonstrated that such a method was able to reach accurate results, preserving the semantic content of the source photograph with very few artifacts.