Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915058650447872 |
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| author | Blondin, Célia Guérin, Joris Inagaki, Kelly Longo, Guilherme Berti-Équille, Laure |
| author_facet | Blondin, Célia Guérin, Joris Inagaki, Kelly Longo, Guilherme Berti-Équille, Laure |
| contents | Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures Blondin, Célia Guérin, Joris Inagaki, Kelly Longo, Guilherme Berti-Équille, Laure Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives. |
| title | Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.08228 |