Photography Style Analysis using Convolutional Neural Networks

In this paper, we present a novel dataset of photographs annotated in terms of the respective image aesthetics. We also examine the ability of Convolutional Neural Networks (CNNs) to distinguish between the adopted photography style classes. In particular, we have defined five photography style classification tasks, related to the following aesthetic attributes: Color, Depth of Field (DoF), Palette, Composition and Type. We then followed an annotation procedure using on a set of 1832 photos selected from the Unsplash dataset. Multiple annotators have also been used, in order to measure inter-annotator agreement. As soon as the dataset was compiled, we trained and evaluated a Residual Neural Network (ResNet50). The experimental results prove that, despite the imbalanced dataset, our model was able to achieve acceptable classification results. The dataset is openly provided, along with the trained models and Python code to use them.