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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder
verfasst von : Bo Yao, Qianzheng Ji, Xiangdong Zhou, Yue Pang, Manliang Cao, Yixuan Wu, Zijing Tan
Erschienen in: Database Systems for Advanced Applications
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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.