Comprehensive material painting feature recognition based on spatial model
Comprehensive material painting is an art form that uses multiple materials and techniques for creation. It combines traditional painting media with non-traditional materials, and this art form has become increasingly common in the field of contemporary art. However, due to the diversity and complex...
| Những tác giả chính: | , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
Elsevier
2025-12-01
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| Loạt: | Systems and Soft Computing |
| Những chủ đề: | |
| Truy cập trực tuyến: | http://www.sciencedirect.com/science/article/pii/S2772941924001108 |
| _version_ | 1826914128298508288 |
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| author | Jing Zhao Aiqin Liu |
| author_facet | Jing Zhao Aiqin Liu |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | Comprehensive material painting is an art form that uses multiple materials and techniques for creation. It combines traditional painting media with non-traditional materials, and this art form has become increasingly common in the field of contemporary art. However, due to the diversity and complexity of comprehensive material painting, traditional visual feature extraction methods are difficult to accurately identify and classify it. To address the above issues, a discriminative color space model is used to operate on the red green blue space, followed by standard processing, and finally Gabor wavelet analysis is performed on each subspace of the red green blue. The experimental results indicated that the model performed well in identification accuracy, recall, and F1 scores. Specifically, the identification accuracy of CMP-FEM reached 95.6 %, which was significantly higher than other contrast models such as IFE-MPA (85.00 %) and CR-GWFE (87.50 %). In addition, the application of the model in the field of painting restoration also showed its strong guiding ability, and the quality of the restored image was significantly improved. According to the comprehensive expert evaluation, the accuracy of the information identification was as high as 95.8 points, and the average F1 score of the repair guidance was 92.7 points, which further confirmed the practicality and accuracy of the model. These results demonstrate the superiority of the comprehensive material painting feature recognition model and provide an effective solution for the identification problem of painting authors. |
| first_indexed | 2025-02-17T10:54:06Z |
| format | Article |
| id | doaj.art-cfa9d7f120924c9ba099adef944c8a1f |
| institution | Directory Open Access Journal |
| issn | 2772-9419 |
| language | English |
| last_indexed | 2025-02-17T10:54:06Z |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj.art-cfa9d7f120924c9ba099adef944c8a1f2024-12-29T04:48:20ZengElsevierSystems and Soft Computing2772-94192025-12-017200181Comprehensive material painting feature recognition based on spatial modelJing Zhao0Aiqin Liu1College of Music, Shangqiu Normal University, Shangqiu 476000, China; Corresponding author.Department of Art and Design, Shanxi Economic and Management Cadre College, Taiyuan 030000, ChinaComprehensive material painting is an art form that uses multiple materials and techniques for creation. It combines traditional painting media with non-traditional materials, and this art form has become increasingly common in the field of contemporary art. However, due to the diversity and complexity of comprehensive material painting, traditional visual feature extraction methods are difficult to accurately identify and classify it. To address the above issues, a discriminative color space model is used to operate on the red green blue space, followed by standard processing, and finally Gabor wavelet analysis is performed on each subspace of the red green blue. The experimental results indicated that the model performed well in identification accuracy, recall, and F1 scores. Specifically, the identification accuracy of CMP-FEM reached 95.6 %, which was significantly higher than other contrast models such as IFE-MPA (85.00 %) and CR-GWFE (87.50 %). In addition, the application of the model in the field of painting restoration also showed its strong guiding ability, and the quality of the restored image was significantly improved. According to the comprehensive expert evaluation, the accuracy of the information identification was as high as 95.8 points, and the average F1 score of the repair guidance was 92.7 points, which further confirmed the practicality and accuracy of the model. These results demonstrate the superiority of the comprehensive material painting feature recognition model and provide an effective solution for the identification problem of painting authors.http://www.sciencedirect.com/science/article/pii/S2772941924001108Spatial modelComprehensive materialsPaintingFeature extraction |
| spellingShingle | Jing Zhao Aiqin Liu Comprehensive material painting feature recognition based on spatial model Systems and Soft Computing Spatial model Comprehensive materials Painting Feature extraction |
| title | Comprehensive material painting feature recognition based on spatial model |
| title_full | Comprehensive material painting feature recognition based on spatial model |
| title_fullStr | Comprehensive material painting feature recognition based on spatial model |
| title_full_unstemmed | Comprehensive material painting feature recognition based on spatial model |
| title_short | Comprehensive material painting feature recognition based on spatial model |
| title_sort | comprehensive material painting feature recognition based on spatial model |
| topic | Spatial model Comprehensive materials Painting Feature extraction |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924001108 |
| work_keys_str_mv | AT jingzhao comprehensivematerialpaintingfeaturerecognitionbasedonspatialmodel AT aiqinliu comprehensivematerialpaintingfeaturerecognitionbasedonspatialmodel |