{"type":"ScholarlyArticle","title":"Revealing Hierarchical Structure of Leaf Venations in Plant Science via Label-Efficient Segmentation: Dataset and Method","authors":[{"name":"Weizhen Liu","slug":"weizhen-liu","given":"Weizhen","family":"Liu","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Ao Li","slug":"ao-li","given":"Ao","family":"Li","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Ze Wu","slug":"ze-wu","given":"Ze","family":"Wu","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Yue Li","slug":"yue-li","given":"Yue","family":"Li","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Baobin Ge","slug":"baobin-ge","given":"Baobin","family":"Ge","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Guangyu Lan","slug":"guangyu-lan","given":"Guangyu","family":"Lan","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Shilin Chen","slug":"shilin-chen","given":"Shilin","family":"Chen","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Minghe Li","slug":"minghe-li","given":"Minghe","family":"Li","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Yunfei Liu","slug":"yunfei-liu","given":"Yunfei","family":"Liu","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Xiaohui Yuan","slug":"xiaohui-yuan","given":"Xiaohui","family":"Yuan","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology; Yazhouwan National Laboratory"},{"name":"Nanqing Dong","slug":"nanqing-dong","given":"Nanqing","family":"Dong","affiliation":"Shanghai Artificial Intelligence Laboratory"}],"abstract":"Hierarchical leaf vein segmentation is a crucial but under-explored task in agricultural sciences, where analysis of the hierarchical structure of plant leaf venation can contribute to plant breeding. While current segmentation techniques rely on data-driven models, there is no publicly available dataset specifically designed for hierarchical leaf vein segmentation. \r\nTo address this gap, we introduce the HierArchical Leaf Vein Segmentation (HALVS) dataset, the first public hierarchical leaf vein segmentation dataset. HALVS comprises 5,057 real-scanned high-resolution leaf images collected from three plant species: soybean, sweet cherry, and London planetree. It also includes human-annotated ground truth for three orders of leaf veins, with a total labeling effort of 83.8 person-days. Based on HALVS, we further develop a label-efficient learning paradigm that leverages partial label information, i.e. missing annotations for tertiary veins. Empirical studies are performed on HALVS, revealing new observations, challenges, and research directions on leaf vein segmentation. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/\r\nHALVS-Hierarchical-Vein-Segment.","doi":"10.24963/ijcai.2024/815","url":"https://ijcai.org/proceedings/2024/815","pdf":"https://ijcai.org/proceedings/2024/0815.pdf","markdown":"https://ijcai.org/proceedings/2024/815.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/815","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":815,"pages":{"first":7367,"last":7375},"keywords":["Multidisciplinary Topics and Applications: General","Machine Learning: General","Computer Vision: General"],"videos":[],"references":[{"position":1,"raw":"[Blonder et al., 2019] Benjamin Blonder, Sabine Both, Miguel Jodra, Noreen Majalap, David Burslem, Yit Arn Teh, and Yadvinder Malhi. 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