The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes
The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/17/3677 |
| _version_ | 1797582672311091200 |
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| author | Le Gao Xin Zhang Tian Yang Baocang Wang Juntao Li |
| author_facet | Le Gao Xin Zhang Tian Yang Baocang Wang Juntao Li |
| author_sort | Le Gao |
| collection | DOAJ |
| description | The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts image analysis technology based on artificial intelligence and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and effectively deal with problems such as the small number of fresco images on the emigrants’ buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes. |
| first_indexed | 2024-03-10T23:24:46Z |
| format | Article |
| id | doaj.art-806df98312a8497bb65f8070df193fb9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:24:46Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-806df98312a8497bb65f8070df193fb92023-11-19T08:02:30ZengMDPI AGElectronics2079-92922023-08-011217367710.3390/electronics12173677The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese FrescoesLe Gao0Xin Zhang1Tian Yang2Baocang Wang3Juntao Li4The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529000, ChinaThe Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529000, ChinaInstitute for Guangdong Qiaoxiang Studies, Wuyi University, Jiangmen 529000, ChinaThe State Key Laboratory of Integrated Service Networks, The Cryptographic Research Center, Xidian University, Xi’an 710071, ChinaSchool of Economics and Management, Wuyi University, Jiangmen 529000, ChinaThe unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts image analysis technology based on artificial intelligence and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and effectively deal with problems such as the small number of fresco images on the emigrants’ buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.https://www.mdpi.com/2079-9292/12/17/3677artificial intelligencedeep learningtransfer learningimage classificationfresco |
| spellingShingle | Le Gao Xin Zhang Tian Yang Baocang Wang Juntao Li The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes Electronics artificial intelligence deep learning transfer learning image classification fresco |
| title | The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes |
| title_full | The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes |
| title_fullStr | The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes |
| title_full_unstemmed | The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes |
| title_short | The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes |
| title_sort | application of resnet 34 model integrating transfer learning in the recognition and classification of overseas chinese frescoes |
| topic | artificial intelligence deep learning transfer learning image classification fresco |
| url | https://www.mdpi.com/2079-9292/12/17/3677 |
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