Document Type
Journal Article
Publication Title
Ecological Informatics
Volume
83
Publisher
Elsevier
School
Centre for Marine Ecosystems Research / School of Science / Centre for Artificial Intelligence and Machine Learning
Abstract
Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL.
DOI
10.1016/j.ecoinf.2024.102804
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Abid, N., Noman, M. K., Kovács, G., Islam, S. M. S., Adewumi, T., Lavery, P., ... & Liwicki, M. (2024). Seagrass classification using unsupervised curriculum learning (UCL). Ecological Informatics, 83, 102804. https://doi.org/10.1016/j.ecoinf.2024.102804