Genre classification of paintings

Extensive digitization efforts in the recent years have led to a large increase of digitized and online available fine-art collections. With digitization of artworks, we aim to preserve all those valuable evidences of various human creative expressions, as well as make them available to a broader audience. The digitalization process of artworks should not constrain only to fulfilling the purpose of preservation, but also serve as a starting point for exploring of this type of data in a novel way, which is made possible with the rise of new achievements in computer vision. In the domain of computer analysis of visual art there are various ongoing research challenges. In this paper, we explore different image feature extraction methods and their applicability in the task of classifying painting by genre. Our dataset includes paintings of various styles grouped in seven genre categories. We achieved an accuracy of 77.57% for the task of genre classification. We concluded that the best performance is achieved when using features derived from a pretrained deep convolutional neural network.