Convolutional Ordinal Regression Forest for Image Ordinal Estimation

doi:10.1109/TNNLS.2021.3055816

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Convolutional Ordinal Regression Forest for Image Ordinal Estimation
	Zhu, Haiping 2; Shan, Hongming 3,4,5; Zhang, Yuheng 2; Che, Lingfu 2; Xu, Xiaoyang 2; Zhang, Junping 2; Shi, Jianbo 6; Wang, Fei-Yue 1,7,8
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-02-18
页码	12
关键词	Decision trees Estimation Task analysis Forestry Vegetation Random forests Support vector machines Differentiable decision trees image ordinal estimation ordinal distribution learning ordinal regression (OR) random forest
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3055816
通讯作者	Zhang, Junping(jpzhang@fudan.edu.cn) ; Wang, Fei-Yue(feiyue@ieee.org)
英文摘要	Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression (OR) problem. Recent methods formulate an OR problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel OR approach, termed convolutional OR forest (CORF), for image ordinal estimation, which can integrate OR and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for OR by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e., facial age estimation and image esthetic assessment, showing significant improvements and better stability over the state-of-the-art OR methods.
资助项目	National Key Research and Development Program of China[2018YFB1305104] ; Shanghai Municipal Science and Technology Major Project[2018SHZDZX01] ; ZhangJiang Lab ; National Natural Science Foundation of China (NSFC)[61533019] ; National Natural Science Foundation of China (NSFC)[61673118] ; National Natural Science Foundation of China[U1811463] ; Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Shanghai Center for Brain Science and Brain-inspired Technology
WOS关键词	RECOGNITION
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:000732901200001
资助机构	National Key Research and Development Program of China ; Shanghai Municipal Science and Technology Major Project ; ZhangJiang Lab ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China ; Key-Area Research and Development Program of Guangdong Province ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Shanghai Center for Brain Science and Brain-inspired Technology
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46806]
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	Zhang, Junping; Wang, Fei-Yue
作者单位	1.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 2.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China 3.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China 4.Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China 5.Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China 6.Univ Penn, GRASP Lab, Philadelphia, PA 19104 USA 7.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 8.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Haiping,Shan, Hongming,Zhang, Yuheng,et al. Convolutional Ordinal Regression Forest for Image Ordinal Estimation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12.
APA	Zhu, Haiping.,Shan, Hongming.,Zhang, Yuheng.,Che, Lingfu.,Xu, Xiaoyang.,...&Wang, Fei-Yue.(2021).Convolutional Ordinal Regression Forest for Image Ordinal Estimation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Zhu, Haiping,et al."Convolutional Ordinal Regression Forest for Image Ordinal Estimation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):12.