1School of Information Management, Nanjing University, Nanjing 210023, China 2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China 3Jinling Library, Nanjing 210023, China
[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.
周泽聿, 王昊, 张小琴, 范涛, 任秋彤. 基于Xception-TD的中华传统刺绣分类模型构建*[J]. 数据分析与知识发现, 2022, 6(2/3): 338-347.
Zhou Zeyu, Wang Hao, Zhang Xiaoqin, Tao Fao, Ren Qiutong. Classification Model for Chinese Traditional Embroidery Based on Xception-TD. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 338-347.
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