Abstract
Plant image recognition is an important thing for protecting plants, protecting the environment, and protecting nature. Recently, most models in the field of plant leaf recognition make classification after extracting global features. In this paper, we propose a plant leaf recognition model based on metric learning. Metric learning calculates the similarity of the extracted feature vectors to obtain the distance between different sample features, so as to determine whether similar pictures belong to the same category, and then achieve the classification effect. In this study, feature triplet are used for metric learning, and the loss function we used is triplet-loss.
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Acknowledgements
This work was supported by the grant of National Key R&D Program of China (No. 2018AAA0100100) and partly supported by National Natural Science Foundation of China (Grant nos. 61861146002, 61520106006, 61732012, 61772370, 61702371, 61932008, 61532008, 61672382, 61772357, and 61672203) and China Postdoctoral Science Foundation (Grant no. 2017M611619) and supported by “BAGUI Scholar” Program and the Scientific & Technological Base and Talent Special Program, GuiKe AD18126015 of the Guangxi Zhuang Autonomous Region of China and supported by Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), LCNBI and ZJLab.
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Yang, H. et al. (2020). Plant Leaf Recognition Network Based on Feature Learning and Metric Learning. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_33
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