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Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification

文献类型：期刊论文

作者Zheng, Wenbo6; Yan, Lan4,5; Gou, Chao3; Wang, Fei-Yue1,2
刊名IEEE TRANSACTIONS ON FUZZY SYSTEMS
出版日期2022-12-01
卷号30期号:12页码:5431-5444
ISSN号1063-6706
关键词Contour feature learning data augmentation deep forest fuzzy logic
DOI10.1109/TFUZZ.2022.3177764
通讯作者Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要Deep learning is a compelling technique for feature extraction due to its adaptive capacity of processing and providing deeper image information. However, for the task of leaf cultivar classification, the deep learning-based classifier model is unable to extract contour features of leaf images deeply due to the lack of large specialized datasets and expert knowledge annotations. Also, the scale/size of the current leaf cultivar dataset does not meet the needs of deep neural networks (DNNs). In particular, the high model complexity of DNNs implies that deep-learning-based neural networks seem to must require a large dataset to achieve good performance, but facing the fact that the leaf cultivar dataset often is small, even some classes in this kind of datasets contain less than ten images/examples. To overcome these problems and inspired by the resounding success of fuzzy logic, we propose a novel fuzzy ensemble model for leaf cultivar classification. To extract the contours of leaves, we first propose generative adversarial networks-based methods. Second, to improve the ability of feature representation, we present a data augmentation method to transform our contour features. Third, to get the essential features of leaves, we design a novel generation of the fuzzy random forest. Finally, to achieve accurate classification, we design a novel deep learning strategy, namely deep fuzzy representation learning, integrating and cascading a lot of our fuzzy random forests. Experimental results show that our model outperforms other existing state-of-the-arts on three real-world datasets, and performs much better than the original deep forest and DNN-based algorithms particularly.
WOS关键词DECISION TREE ; SHAPE ; MODELS ; IDENTIFICATION ; ROTATION
资助项目Natural Science Foundation of China[U1811463] ; Natural Science Foundation of China[61806198] ; National Key R&D Program of China[2018AAA0101502] ; National Key R&D Program of China[2020YFB1600400] ; Key Research and Development Program of Guangzhou[202007050002]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000893027500029
资助机构Natural Science Foundation of China ; National Key R&D Program of China ; Key Research and Development Program of Guangzhou
源URL[http://ir.ia.ac.cn/handle/173211/50830]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Fei-Yue
作者单位1.Natl Univ Def Technol, Res Ctr Mil Computat Expt & Parallel Syst Technol, Changsha 410073, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Sun Yat sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Wuhan Univ Technol, Sch Comp Sci & Artificial Intelligence, Wuhan 430070, Peoples R China
推荐引用方式
GB/T 7714		 Zheng, Wenbo,Yan, Lan,Gou, Chao,et al. Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2022,30(12):5431-5444.
APA Zheng, Wenbo,Yan, Lan,Gou, Chao,&Wang, Fei-Yue.(2022).Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification.IEEE TRANSACTIONS ON FUZZY SYSTEMS,30(12),5431-5444.
MLA Zheng, Wenbo,et al."Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification".IEEE TRANSACTIONS ON FUZZY SYSTEMS 30.12(2022):5431-5444.

入库方式： OAI收割

来源：自动化研究所

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