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

Steven Szu-Chi CHEN; Hui CUI; Ming-han DU; Tie-ming FU; Xiao-hong SUN; Yi JI; Henry DUH

doi:10.1631/FITEE.1900399

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

Special Feature on Intelligent Design | Updated：2022-05-19

- Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network
- 基于机器学习的广彩瓷图案生成系统
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 12, Pages: 1632-1643(2019)
- Affiliations：
  
  Department of Computer Science and Information Technology, La Trobe University, Melbourne 3086, Australia
  School of Art and Design, Guangdong University of Technology, Guangzhou 510006, China
- Author bio：
  
  Yi JI, E-mail: jiyi001@hotmail.com
- Funds：
- DOI：10.1631/FITEE.1900399
  CLC： TP751
- Received：06 August 2019，
  
  Revised：2019-12-12，
  
  Published：2019-12
- Accepted：
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Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, et al. 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.
DOI：

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, et al. 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. DOI： 10.1631/FITEE.1900399.

摘要

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.

关键词

Keywords

references

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