基于卷积神经网络的多肉植物细粒度图像分类

doi:10.12066/j.issn.1007-2861.2029

Abstract

Abstract:

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${\%}$.

Key words: fine-grained image classification, supervised classification, weakly supervised classification, convolutional neural networks (CNNs), AlexNet, GoogLeNet, fine-tuning

CLC Number:

TP183

HUANG Jiabao, ZHU Yonghua, ZHOU Jiting, GAO Wenjing. Fine-grained image classification of succulents with convolutions[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(2): 283-291.

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Fine-grained image classification of succulents with convolutions

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