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Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples

文献类型：期刊论文

作者Huang, Xinwei1; Li, Bing2,3; Li, Shuai1; Li, Wenjuan2,3; Xiong, Weihua2,3; Yin, Xuanwu4; Hu, Weiming5,6; Qin, Hong7
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2020
卷号29页码:7875-7888
关键词Color constancy illumination estimation white balancing multi-cue semi-supervised
ISSN号1057-7149
DOI10.1109/TIP.2020.3007823
通讯作者Li, Bing(bli@nlpr.ia.ac.cn)
英文摘要Color constancy is one of the fundamental tasks in computer vision. Many supervised methods, including recently proposed Convolutional Neural Networks (CNN)-based methods, have been proved to work well on this problem, but they often require a sufficient number of labeled data. However, it is expensive and time-consuming to collect a large number of labeled training images with accurately measured illumination. In order to reduce the dependence on labeled images and leverage unlabeled ones without measured illumination, we propose a novel semi-supervised framework with limited training samples for illumination estimation. Our key insight is that the images with similar features from different cues will share similar lighting conditions. Consequently, three graphs based on three visual cues, low-level RGB color distribution, mid-level initial illuminant estimates and high-level scene content, are constructed to represent the relationship among different images. Then a multi-cue semi-supervised color constancy method (MSCC) is proposed after integrating these three graphs into a unified model. Extensive experiments on benchmark datasets demonstrate that our proposed MSCC method outperforms nearly all the existing supervised methods with limited labeled samples. Even with no unlabeled samples, MSCC still obtains better performance and stableness than most supervised methods.
WOS关键词ILLUMINATION CHROMATICITY ; ALGORITHMS
资助项目Beijing Natural Science Foundation[JQ18018] ; Beijing Natural Science Foundation[L172051] ; National Key Research and Development Program of China[2017YFB1002801] ; National Key Research and Development Program of China[2016QY01W0106] ; National Key Research and Development Program of China[2017YFF0106407] ; National Natural Science Foundation of China[U1936204] ; National Natural Science Foundation of China[U1803119] ; National Natural Science Foundation of China[U1736106] ; National Natural Science Foundation of China[61532002] ; National Natural Science Foundation of China[61906192] ; National Natural Science Foundation of China[61876100] ; USA NSF[IIS-1715985] ; USA NSF[1812606] ; Youth Innovation Promotion Association, CAS
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000552264300004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Beijing Natural Science Foundation ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; USA NSF ; Youth Innovation Promotion Association, CAS
源URL[http://ir.ia.ac.cn/handle/173211/40209]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Li, Bing
作者单位1.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
2.Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
3.PeopleAI Inc, Beijing 100190, Peoples R China
4.Hisilicon, Dept Kirin Chipset & Technol Dev, Beijing 100095, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
7.SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA
推荐引用方式
GB/T 7714		 Huang, Xinwei,Li, Bing,Li, Shuai,et al. Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:7875-7888.
APA Huang, Xinwei.,Li, Bing.,Li, Shuai.,Li, Wenjuan.,Xiong, Weihua.,...&Qin, Hong.(2020).Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,7875-7888.
MLA Huang, Xinwei,et al."Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):7875-7888.

入库方式： OAI收割

来源：自动化研究所

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