ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention
Addressing the difficulties and challenges faced by current traditional digital painting image style classification methods, the study enhances the residual neural network model by incorporating a three-branch convolutional attention mechanism. Furthermore, it integrates the improved residual neural...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000076 |
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| _version_ | 1823856798813126656 |
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| author | Xiaohong Wang Qian Ye Lei Liu Haitao Niu Bangbang Du |
| author_facet | Xiaohong Wang Qian Ye Lei Liu Haitao Niu Bangbang Du |
| author_sort | Xiaohong Wang |
| collection | DOAJ |
| description | Addressing the difficulties and challenges faced by current traditional digital painting image style classification methods, the study enhances the residual neural network model by incorporating a three-branch convolutional attention mechanism. Furthermore, it integrates the improved residual neural network model with a fine-grained image classification model, ultimately presenting a novel approach for digital painting image style classification. The experimental results show that the final model can reach 100%, 98.61%, and 99.31% for the image classification precision, recall, and F1 value of ancient Greek pottery style, respectively. The improved residual neural network model proposed in this study has significant advantages in the task of digital painting image style classification, and can provide an efficient and reliable solution for classifying and recognizing digital painting image styles. |
| format | Article |
| id | doaj-art-0e60ec7206e043889315d964deaae3f5 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-0e60ec7206e043889315d964deaae3f52025-02-12T05:30:44ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100614ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attentionXiaohong Wang0Qian Ye1Lei Liu2Haitao Niu3Bangbang Du4School of Architecture and Design, Yangtze University College of Arts and Sciences, Jingzhou 434200, PR ChinaCorresponding author.; School of Architecture and Design, Yangtze University College of Arts and Sciences, Jingzhou 434200, PR ChinaSchool of Architecture and Design, Yangtze University College of Arts and Sciences, Jingzhou 434200, PR ChinaSchool of Architecture and Design, Yangtze University College of Arts and Sciences, Jingzhou 434200, PR ChinaSchool of Architecture and Design, Yangtze University College of Arts and Sciences, Jingzhou 434200, PR ChinaAddressing the difficulties and challenges faced by current traditional digital painting image style classification methods, the study enhances the residual neural network model by incorporating a three-branch convolutional attention mechanism. Furthermore, it integrates the improved residual neural network model with a fine-grained image classification model, ultimately presenting a novel approach for digital painting image style classification. The experimental results show that the final model can reach 100%, 98.61%, and 99.31% for the image classification precision, recall, and F1 value of ancient Greek pottery style, respectively. The improved residual neural network model proposed in this study has significant advantages in the task of digital painting image style classification, and can provide an efficient and reliable solution for classifying and recognizing digital painting image styles.http://www.sciencedirect.com/science/article/pii/S1110866525000076ResNet-50NTSAttention mechanismDigital painting imagesStyle classificationFuzzy pooling |
| spellingShingle | Xiaohong Wang Qian Ye Lei Liu Haitao Niu Bangbang Du ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention Egyptian Informatics Journal ResNet-50 NTS Attention mechanism Digital painting images Style classification Fuzzy pooling |
| title | ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention |
| title_full | ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention |
| title_fullStr | ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention |
| title_full_unstemmed | ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention |
| title_short | ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention |
| title_sort | resnet 50 nts digital painting image style classification based on three branch convolutional attention |
| topic | ResNet-50 NTS Attention mechanism Digital painting images Style classification Fuzzy pooling |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000076 |
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