收稿日期: 2018-03-09
网络出版日期: 2020-04-29
基金资助
十三五规划重点研究发展计划资助项目(2017YFD0400101)
Fine-grained image classification of succulents with convolutions
Received date: 2018-03-09
Online published: 2020-04-29
黄嘉宝, 朱永华, 周霁婷, 高文靖 . 基于卷积神经网络的多肉植物细粒度图像分类[J]. 上海大学学报(自然科学版), 2020 , 26(2) : 283 -291 . DOI: 10.12066/j.issn.1007-2861.2029
Succulents recognition is an important task in plants identification and management, and domain-specific features and large datasets are used. Due to the absence of an existing succulents' dataset, a large number of pictures need to be collected. This paper addresses the issue of fine-grained image classification of succulent plants. Both supervised and weakly supervised fine-tuning of AlexNet and GoogLeNet training on original dataset were imple mented, trained and tested for the task of identifying the class of succulents from various viewpoints, backgrounds, light effects and growth stages. Results showed that supervised fine-tuning GoogLeNet improved the performance, and the accuracy rate could be as high as 96.7${\%}$.
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