Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification | |
Zheng, Wenbo6; Yan, Lan4,5; Gou, Chao3; Wang, Fei-Yue1,2 | |
刊名 | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
2022-12-01 | |
卷号 | 30期号:12页码:5431-5444 |
关键词 | Contour feature learning data augmentation deep forest fuzzy logic |
ISSN号 | 1063-6706 |
DOI | 10.1109/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. |
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关键词 | DECISION TREE ; SHAPE ; MODELS ; IDENTIFICATION ; ROTATION |
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 |
内容类型 | 期刊论文 |
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. |
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