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Fine-art painting classification via two-channel dual path networks

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Abstract

Fine-art painting expresses the state of mind and social culture of mankind. Automatic fine-art painting classification is an important task to assist the analysis of fine-art paintings. In this paper, we propose a novel two-channel dual path networks for the task of style, artist and genre classification on fine-art painting image. It includes the RGB and the brush stroke information channels. Besides the RGB information channel is used to represent the color information in fine-art painting images, the brush stroke information channel is used to extract brush stroke information from fine-art painting images. And the four-directional gray-level co-occurrence matrix is used in deep learning to detect the brush stroke information, which has never been considered in the task of fine-art painting classification. Experiments on two datasets demonstrate that the four-directional gray-level co-occurrence matrix is effective in feature representation of fine-art painting images. And the proposed model achieves best classification accuracy and good generalization performance when compared with other methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61620106008), the Natural Science Foundation of Guangdong Province (Nos. 2016A030310053, 2016A030310039, 2017A030310521), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20160422151736824), the Shenzhen high-level overseas talents program, the National Engineering Laboratory for Big Data System Computing Technology, the Inlife-Handnet Open Fund, and the Postgraduate Innovation Development Fund Project of Shenzhen University (No. PIDFPZR2018001).

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Zhong, Sh., Huang, X. & Xiao, Z. Fine-art painting classification via two-channel dual path networks. Int. J. Mach. Learn. & Cyber. 11, 137–152 (2020). https://doi.org/10.1007/s13042-019-00963-0

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