02121nas a2200253   4500000000100000000000100001000000100002008004100003653002000044653002300064653002200087653001300109100001200122700001100134700001500145700001100160700001500171245008100186856016200267300001200429490000600441520140000447022002001847       2022                            d10aComputer Vision10aDigital humanities10aTransfer learning10aXception1 aZ. Zeyu1 aW. Hao1 aZ. Xiaoqin1 aT. Fao1 aR. Qiutong00aClassification Model for Chinese Traditional Embroidery Based on Xception-TD  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130454537&doi=10.11925%2finfotech.2096-3467.2021.0909&partnerID=40&md5=ef8593b9fa5292928b03e764a91cebbe  a338-3470 v63 a[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.  a20963467 (ISSN)