Classification of basic artistic media based on a deep convolutional approach

Heekyung Yang, Kyungha Min

Abstract: Artistic media play an important role in recognizing and classifying artworks in many artwork classification works and public artwork databases. We employ deep CNN structure to recognize artistic media from artworks and to classify them into predetermined categories. For this purpose, we define basic artistic media as oilpaint brush, pastel, pencil and watercolor and build artwork image dataset by collecting artwork images from various websites. To build our classifier, we implement various recent deep CNN structures and compare their performances. Among them, we select DenseNet, which shows best performance for recognizing artistic media. Through the human baseline experiment, we show that the performance of our classifier is compatible with that of trained human. Furthermore, we also show that our classifier shows a similar recognition and classification pattern with human in terms of well-classifying media, ill-classifying media, confusing pair and confusing case. We also collect synthesized oilpaint artwork images from fourteen important oilpaint literatures and apply them to our classifier. Our classifier shows a meaningful performance, which will lead to an evaluation scheme for the artistic media simulation techniques of non-photorealistic rendering (NPR) society.