Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures

Autor:	Blondin, Célia, Guérin, Joris, Inagaki, Kelly, Longo, Guilherme, Berti-Équille, Laure
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Poster at Tackling Climate Change with Machine Learning: workshop at NeurIPS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.08228 Zobrazit plný text záznamu View this record from Arxiv