Classification of Artistic Styles using Binarized Features Derived from a Deep Neural Network

Abstract. With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural net-works has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCodes descriptors, these features show excellent classification results on a large scale collection of paintings.