Knowledge Commons of Institute of Automation,CAS
Convolutional Ordinal Regression Forest for Image Ordinal Estimation | |
Zhu, Haiping1; Shan, Hongming2,3,4; Zhang, Yuheng1; Che, Lingfu1; Xu, Xiaoyang1; Zhang, Junping1; Shi, Jianbo5; Wang, Fei-Yue6,7,8 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2021-02-18 | |
页码 | 12 |
通讯作者 | Zhang, Junping(jpzhang@fudan.edu.cn) ; Wang, Fei-Yue(feiyue@ieee.org) |
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. |
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 |
DOI | 10.1109/TNNLS.2021.3055816 |
关键词[WOS] | RECOGNITION |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | 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 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000732901200001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46807 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Zhang, Junping; Wang, Fei-Yue |
作者单位 | 1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China 2.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China 3.Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China 4.Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China 5.Univ Penn, GRASP Lab, Philadelphia, PA 19104 USA 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 7.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 8.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论