[1]林丽惠,罗志明,王军政,等.融合整体与局部信息的武夷岩茶叶片分类方法[J].智能系统学报,2020,15(5):919-924.[doi:10.11992/tis.202003018]
LIN Lihui,LUO Zhiming,WANG Junzheng,et al.Classification of Wuyi rock tealeaves by integrating global and local information[J].CAAI Transactions on Intelligent Systems,2020,15(5):919-924.[doi:10.11992/tis.202003018]
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LIN Lihui,LUO Zhiming,WANG Junzheng,et al.Classification of Wuyi rock tealeaves by integrating global and local information[J].CAAI Transactions on Intelligent Systems,2020,15(5):919-924.[doi:10.11992/tis.202003018]
融合整体与局部信息的武夷岩茶叶片分类方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第5期
页码:
919-924
栏目:
学术论文—机器学习
出版日期:
2020-09-05
- Title:
- Classification of Wuyi rock tealeaves by integrating global and local information
- Keywords:
- classification of Wuyi rock tealeaves; deep learning; transfer learning; feature integration; convolutional neural network; residual network; edge shape; texture
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
-
针对武夷岩茶鲜茶叶叶片图像分类问题，提出一种融合整体与局部信息的分类方法。该方法使用两分支并行结构构建了一个整体与局部信息融合的卷积神经网络模型。实验表明，在9个品种共计
7330张武夷岩茶鲜茶叶叶片图像数据集上，基于ResNet18构造的两分支并行卷积神经网络模型的分类准确率为96.68%，超过了其他CNN模型的分类准确率。这表明通过融合全局信息、边缘形状信息和纹理局部信息能有效提高分类准确率。
- Abstract:
-
In this study, we focused on the classification of fresh Wuyi rock tealeaf images into different fine-grained categories and the construction of a two-branch parallel-structured convolutional neural network (CNN) model by integrating global and local information. We constructed a Wuyi rock tealeaf image dataset comprising
7330 fresh tealeaf images of nine varieties. The experimental results showed that the proposed two-branch parallel-structured CNN model with ResNet18 achieved an accuracy of 96.68% on the Wuyi rock tealeaf image dataset, which is superior to that of other CNN models. This result demonstrates that integrating global information and local information relating to edge shape and texture can effectively improve classification accuracy.
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备注/Memo
收稿日期:2020-03-12。
基金项目:国家自然科学基金项目（61876159，61806172，U1705286）；福建省2011协同创新中心—中国乌龙茶产业协同创新中心专项（闽教科〔2015〕75号）；福建省自然科学基金项目（2017J01780，2018J01562，2020J01421）；武夷学院认知计算与智能信息处理福建省高校重点实验室开放课题项目（KLCCIIP2018105，KLCCIIP2018201）
作者简介:林丽惠,副教授,主要研究方向为图像处理和机器学习。主持或参与福建自然科学基金项目多项。表学术论文10余篇;罗志明,博士研究生,主要研究方向为图像分割、目标检测、医学图像分析。发表学术论文20余篇;李绍滋,教授,博士生导师,主要研究方向为计算机视觉、机器学习。主持或参与国家863项目、国家自然科学基金项目多项。发表学术论文300余篇
通讯作者:李绍滋.E-mail:szlig@xmu.edu.cn
更新日期/Last Update:
2021-01-15