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Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures

Domain:

environment and energyclimate

Record type:

paper
Creator:
BloGuéInaLon
Host:avatar
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. Poster at Tackling Climate Change with Machine Learning: workshop at NeurIPS 2024

Visit

arxiv.org

Tasks

computer visionimage classification

Tags

Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning

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