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.
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Acknowledgements
This work was supported by NSFC grant No. 61370157, NSFC grant No. 61572135 and Shanghai Innovation Action Project No. 17DZ1203-600.
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Yao, B. et al. (2019). Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_25
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DOI: https://doi.org/10.1007/978-3-030-18590-9_25
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