Full Text:   <230>

CLC number: TP751

On-line Access: 2020-01-13

Received: 2019-08-06

Revision Accepted: 2019-11-18

Crosschecked: 2019-12-12

Cited: 0

Clicked: 617

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Steven Szu-Chi Chen

http://orcid.org/0000-0001-6019-7034

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.12 P.1632-1643

10.1631/FITEE.1900399


Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network


Author(s):  Steven Szu-Chi Chen, Hui Cui, Ming-han Du, Tie-ming Fu, Xiao-hong Sun, Yi Ji, Henry Duh

Affiliation(s):  Department of Computer Science and Information Technology, La Trobe University, Melbourne 3086, Australia; more

Corresponding email(s):   jiyi001@hotmail.com

Key Words:  Cantonese porcelain, Classification, Generative adversarial network, Creative arts


Steven Szu-Chi Chen, Hui Cui, Ming-han Du, Tie-ming Fu, Xiao-hong Sun, Yi Ji, Henry Duh. Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(12): 1632-1643.

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author="Steven Szu-Chi Chen, Hui Cui, Ming-han Du, Tie-ming Fu, Xiao-hong Sun, Yi Ji, Henry Duh",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="12",
pages="1632-1643",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900399"
}

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%A Hui Cui
%A Ming-han Du
%A Tie-ming Fu
%A Xiao-hong Sun
%A Yi Ji
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A1 - Xiao-hong Sun
A1 - Yi Ji
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Abstract: 
Accurate recognition of modern and traditional porcelain styles is a challenging issue in cantonese porcelain management due to the large variety and complex elements and patterns. We propose a hybrid system with porcelain style identification and image recreation modules. In the identification module, prediction of an unknown porcelain sample is obtained by logistic regression of ensembled neural networks of top-ranked design signatures, which are obtained by discriminative analysis and transformed features in principal components. The synthesis module is developed based on a conditional generative adversarial network, which enables users to provide a designed mask with porcelain elements to generate synthesized images of cantonese porcelain. Experimental results of 603 cantonese porcelain images demonstrate that the proposed model outperforms other methods relative to precision, recall, area under curve of receiver operating characteristic, and confusion matrix. Case studies on image creation indicate that the proposed system has the potential to engage the community in understanding cantonese porcelain and promote this intangible cultural heritage.

基于机器学习的广彩瓷图案生成系统

摘要:由于广彩瓷设计元素和图案种类繁多、复杂多样,准确识别现代及传统瓷器风格是广彩瓷传承工作中的巨大挑战。提出一种基于广彩瓷风格识别和图像合成模块的图案生成系统。在识别模块中,通过主成分分析和所提判别冗余量化策略对特征重要性进行分析和排序,然后分别训练两组神经网络,将最优设计特征与转换后的主成分特征关联,最后利用整体神经网络逻辑回归方法预测未知广彩瓷。基于条件生成对抗网络(cGAN)开发合成模块,要求用户提供自己设计的创意掩码或抽象瓷元素图像,以生成新的广彩瓷风格合成图像。在系统开发过程中,使用603幅广彩瓷图像测试分类模型。测试结果表明,所提模型在精确度、召回率、接受者操作特性曲线(ROC)的曲线下面积(AUC)和混淆矩阵等方面均优于其他方法。对用户设计的各种元素合成图像的案例研究表明,该系统有助于提高学习者对广彩瓷的欣赏和艺术创作能力。

关键词:广彩瓷;分类;生成对抗网络;艺术创作

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bao H, Liang Y, Liu HZ, et al., 2010. A novel algorithm for extraction of the scripts part in traditional Chinese painting images. Proc 2nd Int Conf on Software Technology and Engineering, p.V2-26-V2-30.

[2]Buades A, Coll B, Morel JM, 2005. A non-local algorithm for image denoising. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.60-65.

[3]Chen KH, 2019. Image Operations with cGAN. http://www.k4ai.com/imageops/index.html

[4]China Intangible Cultural Heritage Network, 2008. Cantonese Porcelain Inheritance Project. http://www.ihchina.cn/project_details/14453/ [Accessed on July 16, 2019] (in Chinese).

[5]Cochran WG, 1954. Some methods for strengthening the common χ2 tests. Int Biom Soc, 10(4):417-451.

[6]Dirvanauskas D, Maskeliūnas R, Raudonis V, et al., 2019. HEMIGEN: human embryo image generator based on generative adversarial networks. Sensors, 19(16):3578.

[7]Efros AA, Freeman WT, 2001. Image quilting for texture synthesis and transfer. Proc 28th Annual Conf on Computer Graphics and Interactive Techniques, p.341-346.

[8]El Hattami A, Pierre-Doray É, Barsalou Y, 2019. Background removal using U-net, GAN and image matting. https://github.com/eti-p-doray/unet-gan-matting [Accessed on July 14, 2019].

[12]Emami H, Dong M, Nejad-Davarani SP, et al., 2018. Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med Phys, 45(8): 3627-3636.

[9]Goodfellow IJ, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27th Int Conf on Neural Information Processing Systems, p.2672-2680.

[10]Hertzmann A, Jacobs CE, Oliver N, et al., 2001. Image analogies. Proc 28th Annual Conf on Computer Graphics and Interactive Techniques, p.327-340.

[11]Iizuka S, Simo-Serra E, Ishikawa H, 2016. Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans Graph, 35(4), Article 110.

[12]Isola P, Zhu JY, Zhou TH, et al., 2017. Image-to-image translation with conditional adversarial networks. IEEE Conf on Computer Vision and Pattern Recognition, p.1125-1134.

[13]Ji Y, Tan P, Chen SC, et al., 2019. Kansei engineering for E-commerce Cantonese porcelain selection in China. 21st Int Conf on Human-Computer Interaction, p.463-474.

[14]Jiang SQ, Huang QM, Ye QX, et al., 2006. An effective method to detect and categorize digitized traditional Chinese paintings. Patt Recogn Lett, 27(7):734-746.

[15]Kira K, Rendell LA, 1992. A practical approach to feature selection. Machine Learning Proc, p.249-256.

[16]Kurin R, 2004. Safeguarding intangible cultural heritage in the 2003 UNESCO convention: a critical appraisal. Museum Int, 56(1-2):66-77.

[17]Larsson G, Maire M, Shakhnarovich G, 2016. Learning representations for automatic colorization. Proc 14th European Conf on Computer Vision, p.577-593.

[18]Lecoutre A, Négrevergne B, Yger F, 2017. Recognizing art style automatically in painting with deep learning. Proc 9th Asian Conf on Machine Learning, p.327-342.

[19]Li WB, Zhang PC, Zhang L, et al., 2019. Object-driven text-to-image synthesis via adversarial training. https://arxiv.org/abs/1902.10740

[20]Lin TY, Maire M, Belongie S, et al., 2014. Microsoft COCO: common objects in context. Proc 13th European Conf on Computer Vision, p.740-755.

[21]Liu YF, Qin ZC, Wan T, et al., 2018. Auto-painter: cartoon image generation from sketch by using conditional Wasserstein generative adversarial networks. Neurocomputing, 311:78-87.

[22]Lowenthal D, 2005. Natural and cultural heritage. Int J Herit Stud, 11(1):81-92.

[28]Mao XF, Wang SH, Zheng LY, et al., 2018. Semantic invariant cross-domain image generation with generative adversarial networks. Neurocomputing, 293:55-63.

[23]Meng QY, Zhang HH, Zhou MQ, et al., 2018. The classification of traditional Chinese painting based on CNN. Proc 4th Int Conf on Cloud Computing and Security, p.232-241.

[24]Połap D, Woźniak M, Wei W, et al., 2018. Multi-threaded learning control mechanism for neural networks. Fut Gener Comput Syst, 87:16-34.

[25]Quinlan JR, 1986. Induction of decision trees. Mach Learn, 1(1):81-106.

[26]Smith L, Akagawa N, 2008. Intangible Heritage. Routledge, London, UK.

[27]Yu L, Liu H, 2003. Feature selection for high-dimensional data: a fast correlation-based filter solution. Proc 20th Int Conf on Machine Learning, p.856-863.

[28]Zhang R, Isola P, Efros AA, 2016. Colorful image colorization. Proc 14th European Conf on Computer Vision, p.649-666.

[29]Zhu JY, Park T, Isola P, et al., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE Int Conf on Computer Vision, p.2223-2232.

[30]Zujovic J, Gandy L, Friedman S, et al., 2009. Classifying paintings by artistic genre: an analysis of features & classifiers. IEEE Int Workshop on Multimedia Signal Processing, p.1-5.

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