Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder

Abstract

In the theory of art, composition is based on the placement or arrangement of visual elements or ingredients in a painting to express the thoughts of the artist. Inspired by that, we propose a novel approach called Semantic Variational Autoencoder (SemanticVAE) to deal with the problem of ancient Chinese landscape painting composition classification. Extensive experiments are conducted on a real ancient Chinese landscape painting image dataset collected from museums. The experimental results show that, in contrast to the state-of-the-art deep CNNs, our method significantly improves the performance of ancient Chinese landscape painting composition classification.