用分层分类提升珊瑚礁底质图像自动标注精度
Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures
- 采用分层分类框架，捕捉珊瑚的分类与健康层级关系
- 在巴西东北部珊瑚礁数据集上，F1和分层F1均提升约2%
- 更符合生态监测需求，适合海洋保护研究者使用
自动化底质图像标注对有效监测和保护珊瑚礁应对气候变化至关重要。当前机器学习方法未能捕捉覆盖珊瑚基底的底栖生物的层次结构，即珊瑚分类等级和健康状况。为解决这一局限，我们提出使用分层分类进行底质图像标注。在来自巴西东北部珊瑚礁的自建数据集上的实验表明，该方法优于平坦分类器，在不同训练数据量下，F1分数和分层F1分数均提升约2%。此外，该分层方法更契合生态学目标。
原文摘要 · Abstract (English)
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.
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