[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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融合整体与局部信息的武夷岩茶叶片分类方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
919-924
栏目:
学术论文—机器学习
出版日期:
2020-10-31

文章信息/Info

Title:
Classification of Wuyi rock tealeaves by integrating global and local information
作者:
林丽惠12 罗志明23 王军政4 李绍滋4
1. 武夷学院 数学与计算机学院,福建 武夷山 354300;
2. 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,福建 武夷山 354300;
3. 厦门大学 信息与通信工程博士后流动站,福建 厦门 361005;
4. 厦门大学 信息科学与技术学院,福建 厦门 361005
Author(s):
LIN Lihui12 LUO Zhiming23 WANG Junzheng4 LI Shaozi4
1. School of Mathematics and Computer Science, Wuyi University, Wuyishan 354300, China;
2. The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University, Wuyishan 354300, China;
3. Post-Doctoral Mobile Station of Information and Communication Engineering, Xiamen University, Xiamen 361005, China;
4. Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China
关键词:
武夷岩茶叶片分类深度学习迁移学习特征融合卷积神经网络残差网络边缘形状纹理
Keywords:
classification of Wuyi rock tealeavesdeep learningtransfer learningfeature integrationconvolutional neural networkresidual networkedge shapetexture
分类号:
TP391
DOI:
10.11992/tis.202003018
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.

参考文献/References:

[1] 张宁, 刘文萍. 基于图像分析的植物叶片识别技术综述[J]. 计算机应用研究, 2011, 28(11): 4001-4007
ZHANG Ning, LIU Wenping. Plant leaf recognition technology based on image analysis[J]. Application research of computers, 2011, 28(11): 4001-4007
[2] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016, 31(1): 1-17
LU Hongtao, ZHANG Qinchuan. Applications of deep convolutional neural network in computer vision[J]. Journal of data acquisition and processing, 2016, 31(1): 1-17
[3] 王成济, 罗志明, 钟准, 等. 一种多层特征融合的人脸检测方法[J]. 智能系统学报, 2018, 13(1): 138-146
WANG Chengji, LUO Zhiming, ZHONG Zhun, et al. Face detection method fusing multi-layer features[J]. CAAI transactions on intelligent systems, 2018, 13(1): 138-146
[4] 周俊宇, 赵艳明. 卷积神经网络在图像分类和目标检测应用综述[J]. 计算机工程与应用, 2017, 53(13): 34-41
ZHOU Junyu, ZHAO Yanming. Application of convolution neural network in image classification and object detection[J]. Computer engineering and applications, 2017, 53(13): 34-41
[5] 刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001
LIU Dawei, HAN Ling, HAN Xiaoyong. High spatial resolution remote sensing image classification based on deep learning[J]. Acta optica sinica, 2016, 36(4): 0428001
[6] 李亚飞, 董红斌. 基于卷积神经网络的遥感图像分类研究[J]. 智能系统学报, 2018, 13(4): 550-556
LI Yafei, DONG Hongbin. Classification of remote-sensing image based on convolutional neural network[J]. CAAI transactions on intelligent systems, 2018, 13(4): 550-556
[7] 刘彪, 黄蓉蓉, 林和, 等. 基于卷积神经网络的盲文音乐识别研究[J]. 智能系统学报, 2019, 14(1): 186-193
LIU Biao, HUANG Rongrong, LIN He, et al. Research on braille music recognition based on convolutional neural networks[J]. CAAI transactions on intelligent systems, 2019, 14(1): 186-193
[8] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251
ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinese journal of computers, 2017, 40(6): 1229-1251
[9] YALCIN H, RAZAVI S. Plant classification using convolutional neural networks[C]//2016 Fifth International Conference on Agro-Geoinformatics. Tianjin, China, 2016: 1-5.
[10] LEE S H, CHAN C S, MAYO S J, et al. How deep learning extracts and learns leaf features for plant classification[J]. Pattern recognition, 2017, 71: 1-13.
[11] GRINBLAT G L, UZAL L C, LARESE M G, et al. Deep learning for plant identification using vein morphological patterns[J]. Computers and electronics in agriculture, 2016, 127: 418-424.
[12] PAWARA P, OKAFOR E, SURINTA O, et al. Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition[C]//6th International Conference on Pattern recognition Applications and Methods. Porto, Portugal, 2017: 479-486.
[13] LIN Lihui, LI C, YANG Sheng, et al. Automated classification of Wuyi rock tealeaves based on support vector machine[J]. Concurrency and computation: practice and experience, 2019, 31(23): e4519.
[14] PANDOLFI C, MUGNAI S, AZZARELLO E, et al. Artificial neural networks as a tool for plant identification: a case study on vietnamese tea accessions[J]. Euphytica, 2009, 166(3): 411-421.
[15] 陈怡群, 常春, 肖宏儒, 等. 人工神经网络技术在鲜茶叶分选中的应用[J]. 农业网络信息, 2010(7): 37-40, 43
CHEN Yiqun, CHANG Chun, XIAO Hongru, et al. Artificial neural networks technology in the fresh tea sorting[J]. Agriculture network information, 2010(7): 37-40, 43
[16] 刘自强. 鲜茶叶图像特征提取及在茶树品种识别中的应用研究[D]. 长沙: 湖南农业大学, 2014.
LIU Ziqiang. Features extraction of fresh tea images and its application on the recognition of tea varieties[D]. Changsha: Hunan Agricultural University, 2014.
[17] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, USA, 2012: 1097-1105.
[18] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.
[19] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 1-9.
[20] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 770-778.
[21] 胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨论[J]. 智能系统学报, 2019, 14(1): 1-19
HU Yue, LUO Dongyang, HUA Kui, et al. Overview on deep learning[J]. CAAI transactions on intelligent systems, 2019, 14(1): 1-19
[22] 庄福振, 罗平, 何清, 等. 迁移学习研究进展[J]. 软件学报, 2015, 26(1): 26-39
ZHUANG Fuzhen, LUO Ping, HE Qing, et al. Survey on transfer learning research[J]. Journal of software, 2015, 26(1): 26-39

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备注/Memo

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