Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction me...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316996 |
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| _version_ | 1832540221479059456 |
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| author | Hilman Nordin Bushroa Abdul Razak Norrima Mokhtar Mohd Fadzil Jamaludin Adeel Mehmood |
| author_facet | Hilman Nordin Bushroa Abdul Razak Norrima Mokhtar Mohd Fadzil Jamaludin Adeel Mehmood |
| author_sort | Hilman Nordin |
| collection | DOAJ |
| description | Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. This innovative method has the potential to transform the approach to managing mold defects in fine art paintings by offering a more precise and efficient means of identification. By enabling early detection of mold defects, this method can play a crucial role in safeguarding these invaluable artworks for future generations. |
| format | Article |
| id | doaj-art-f340d32708d94c69bd6ece883594cab8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-f340d32708d94c69bd6ece883594cab82025-02-05T05:32:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031699610.1371/journal.pone.0316996Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.Hilman NordinBushroa Abdul RazakNorrima MokhtarMohd Fadzil JamaludinAdeel MehmoodMold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. This innovative method has the potential to transform the approach to managing mold defects in fine art paintings by offering a more precise and efficient means of identification. By enabling early detection of mold defects, this method can play a crucial role in safeguarding these invaluable artworks for future generations.https://doi.org/10.1371/journal.pone.0316996 |
| spellingShingle | Hilman Nordin Bushroa Abdul Razak Norrima Mokhtar Mohd Fadzil Jamaludin Adeel Mehmood Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. PLoS ONE |
| title | Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. |
| title_full | Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. |
| title_fullStr | Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. |
| title_full_unstemmed | Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. |
| title_short | Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. |
| title_sort | automated mold defects classification in paintings a comparison of machine learning and rule based techniques |
| url | https://doi.org/10.1371/journal.pone.0316996 |
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