TY - JOUR
KW - Computer Vision
KW - Digital humanities
KW - Transfer learning
KW - Xception
AU - Z. Zeyu
AU - W. Hao
AU - Z. Xiaoqin
AU - T. Fao
AU - R. Qiutong
AB - [Objective] This paper introduces artificial intelligence methods to the field of digital humanities, aiming to address the issues of small data sets, insufficient image feature representation, and low recognition accuracy facing traditional Chinese embroidery image classification. It also tries to prvovide methodology support to the digitalization of intangible cultural heritage protection. [Methods] We utilized deep learning techniques to analyze the embroidery images, and extracted their features. Then, we fine-tuned the Xception model with the migration learning approach, and constructed a Xception-TD method to classify traditional Chinese embroidery. Finally, we explored the impacts of the number and dimensions of fully connected layers, as well as the value of dropouts on the model’s performance. [Results] We found that increasing the number and dimensions of fully connected layers improved the embroidery image feature representation. The accuracy rate of our new model reached 0.96863, which was better than the benchmark model. In multi-classification tasks, the model’ s accuracy was also better than that of the benchmark ones. [Limitations] The experimental data set was only constructed with Baidu images, which had small amount of manual taggings. [Conclusions] The proposed model based on transfer learning could improve the accuracy of embroidery classification.
BT - Data Analysis and Knowledge Discovery
DO - 10.11925/infotech.2096-3467.2021.0909
LA - Chinese
M1 - 2-3
N1 - Publisher: Chinese Academy of Sciences
N2 - [Objective] This paper introduces artificial intelligence methods to the field of digital humanities, aiming to address the issues of small data sets, insufficient image feature representation, and low recognition accuracy facing traditional Chinese embroidery image classification. It also tries to prvovide methodology support to the digitalization of intangible cultural heritage protection. [Methods] We utilized deep learning techniques to analyze the embroidery images, and extracted their features. Then, we fine-tuned the Xception model with the migration learning approach, and constructed a Xception-TD method to classify traditional Chinese embroidery. Finally, we explored the impacts of the number and dimensions of fully connected layers, as well as the value of dropouts on the model’s performance. [Results] We found that increasing the number and dimensions of fully connected layers improved the embroidery image feature representation. The accuracy rate of our new model reached 0.96863, which was better than the benchmark model. In multi-classification tasks, the model’ s accuracy was also better than that of the benchmark ones. [Limitations] The experimental data set was only constructed with Baidu images, which had small amount of manual taggings. [Conclusions] The proposed model based on transfer learning could improve the accuracy of embroidery classification.
PY - 2022
SP - 338
EP - 347
T2 - Data Analysis and Knowledge Discovery
TI - Classification Model for Chinese Traditional Embroidery Based on Xception-TD
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130454537&doi=10.11925%2finfotech.2096-3467.2021.0909&partnerID=40&md5=ef8593b9fa5292928b03e764a91cebbe
VL - 6
SN - 20963467 (ISSN)
ER -