Abstract
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 deep residual network to classify fine-art painting images. In detail, we take the advantage of the ImageNet to pre-train the deep residual network. Our two channels include the RGB channel and the brush stroke information channel. The gray-level co-occurrence matrix is used to detect the brush stroke information, which has never been considered in the task of fine-art painting classification. Experiments demonstrate that the proposed model achieves better classification performance than other models. Moreover, each stage of our model is effective for the image classification.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61502311), the Natural Science Foundation of Guangdong Province (No. 2016A030310053), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20150324141711640, JCYJ20160422151736824), the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), the Shenzhen high-level overseas talents program, and the Tencent “Rhinoceros Birds” - Scientific Research Foundation for Young Teachers of Shenzhen University (2015, 2016).
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Huang, X., Zhong, Sh., Xiao, Z. (2018). Fine-Art Painting Classification via Two-Channel Deep Residual Network. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_8
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