Identifying Chinese Ceramic Genres Based on Image Modal Transfer and Ensemble Learning
Shi Bin1,2,Wang Hao1,2(),Deng Sanhong1,2
1School of Information Management, Nanjing University, Nanjing 210023, China 2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper constructs a clique recognition model for Chinese ceramic images. It aims to automatically classify and recognize the clique of ceramic images and provide technical support for the research and digital protection of ceramic culture. [Methods] We adopted the “end-to-end learning” paradigm to build the new model. It applied transfer learning and ensemble learning technology to ceramic cliff identification. We also used the DCGAN algorithm to balance samples. We examined the new model with ten cliques of ceramics based on their types, crafts, and artistic styles. [Results] The proposed model could more effectively extract ceramic image features and recognize ceramic cliques than the baseline models with manually designed feature engineering. Transfer learning enables the extracted features to be effectively transferred to the fine-grained downstream tasks. The accuracy of the new model reached 73.16%. The improved Stacking method integrated knowledge from the proposed models and increased the final accuracy to 81.39%. [Limitations] The data used in this paper is from Baidu pictures, which need to be expanded to improve the model’s performance. [Conclusions] The new model could effectively classify and identify ceramic images.
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Shi Bin, Wang Hao, Deng Sanhong. Identifying Chinese Ceramic Genres Based on Image Modal Transfer and Ensemble Learning. Data Analysis and Knowledge Discovery, 2023, 7(12): 88-101.
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