[1]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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Classification of Wuyi rock tealeaves by integrating global and local information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 919-924 Column: 学术论文—机器学习 Public date: 2020-09-05
Title:
Classification of Wuyi rock tealeaves by integrating global and local information
Author(s):
LIN Lihui1; 2;  LUO Zhiming2; 3;  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
Keywords:
classification of Wuyi rock tealeaves; deep learning; transfer learning; feature integration; convolutional neural network; residual network; edge shape; texture
CLC:
TP391
DOI:
10.11992/tis.202003018
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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