Machine Learning Models for Artist Classification of Cultural Heritage Sketches
Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic fea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/212 |
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| author | Gianina Chirosca Roxana Rădvan Silviu Mușat Matei Pop Alecsandru Chirosca |
| author_facet | Gianina Chirosca Roxana Rădvan Silviu Mușat Matei Pop Alecsandru Chirosca |
| author_sort | Gianina Chirosca |
| collection | DOAJ |
| description | Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging task with three machine learning algorithms and evaluate their performance on a small collection of images from five distinct artists. These algorithms aim to find the most appropriate artist for a sketch (or a contour of a sketch), with promising results that have a higher level of confidence (around 92%). Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. The proposed tool provides a base for future improvements to create a tool that aids artwork evaluators. |
| format | Article |
| id | doaj-art-eaf8e8f83f67483d91148f5db396e9af |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-eaf8e8f83f67483d91148f5db396e9af2025-08-20T02:47:00ZengMDPI AGApplied Sciences2076-34172024-12-0115121210.3390/app15010212Machine Learning Models for Artist Classification of Cultural Heritage SketchesGianina Chirosca0Roxana Rădvan1Silviu Mușat2Matei Pop3Alecsandru Chirosca4National Institute for Research and Development in Optoelectronics, 409 Atomiștilor Str., 077125 Măgurele, RomaniaNational Institute for Research and Development in Optoelectronics, 409 Atomiștilor Str., 077125 Măgurele, RomaniaNational Institute for Research and Development in Optoelectronics, 409 Atomiștilor Str., 077125 Măgurele, RomaniaNational Institute for Research and Development in Optoelectronics, 409 Atomiștilor Str., 077125 Măgurele, RomaniaFaculty of Physics, University of Bucharest, 405 Atomiștilor Str., 077125 Măgurele, RomaniaModern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging task with three machine learning algorithms and evaluate their performance on a small collection of images from five distinct artists. These algorithms aim to find the most appropriate artist for a sketch (or a contour of a sketch), with promising results that have a higher level of confidence (around 92%). Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. The proposed tool provides a base for future improvements to create a tool that aids artwork evaluators.https://www.mdpi.com/2076-3417/15/1/212sketch artist attributionmachine learningvision-based algorithm evaluationFaster R-CNNreinforcement learningvector database |
| spellingShingle | Gianina Chirosca Roxana Rădvan Silviu Mușat Matei Pop Alecsandru Chirosca Machine Learning Models for Artist Classification of Cultural Heritage Sketches Applied Sciences sketch artist attribution machine learning vision-based algorithm evaluation Faster R-CNN reinforcement learning vector database |
| title | Machine Learning Models for Artist Classification of Cultural Heritage Sketches |
| title_full | Machine Learning Models for Artist Classification of Cultural Heritage Sketches |
| title_fullStr | Machine Learning Models for Artist Classification of Cultural Heritage Sketches |
| title_full_unstemmed | Machine Learning Models for Artist Classification of Cultural Heritage Sketches |
| title_short | Machine Learning Models for Artist Classification of Cultural Heritage Sketches |
| title_sort | machine learning models for artist classification of cultural heritage sketches |
| topic | sketch artist attribution machine learning vision-based algorithm evaluation Faster R-CNN reinforcement learning vector database |
| url | https://www.mdpi.com/2076-3417/15/1/212 |
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