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: 7191
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.
@article{title="Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network",
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"
}
%0 Journal Article
%T Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network
%A Steven Szu-Chi Chen
%A Hui Cui
%A Ming-han Du
%A Tie-ming Fu
%A Xiao-hong Sun
%A Yi Ji
%A Henry Duh
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 12
%P 1632-1643
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900399
TY - JOUR
T1 - Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network
A1 - Steven Szu-Chi Chen
A1 - Hui Cui
A1 - Ming-han Du
A1 - Tie-ming Fu
A1 - Xiao-hong Sun
A1 - Yi Ji
A1 - Henry Duh
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 12
SP - 1632
EP - 1643
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900399
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.
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