Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning | |
Li, Yinlin1,2; Jia, Lihao3,4; Wang, Zidong5; Qian, Yang3,6; Qiao, Hong1,6,7,8 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2019-03-21 | |
卷号 | 334期号:2019页码:11-24 |
摘要 | With the rapid development of wearable devices and technologies, hand segmentation remains a less explored direction in egocentric vision, which is very important for activity recognition, rehabilitation, robot self-learning, etc. To overcome the high cost of auxiliary equipment and pixel-level annotations, we present an un-supervised hand segmentation method for egocentric images. Firstly, a fully convolutional neural network (FCN) is pre-trained in source dataset containing pixel-level annotations. Then, in target dataset without labels, the network is re-trained with optimized masks, which are produced by modified local and global consistency learning (LLGC) based on pre-segmentation and superpixel features. Finally, hand segmentation is realized in an alternative way. Furthermore, to balance segmentation accuracy and the cost on labeling, we propose a new semi-supervised image segmentation framework with three subnets based on the optimized noisy masks and a small number of clean labeled data. Experimental results in two target datasets indicate that the proposed methods could achieve better performance than other methods. (C) 2019 Elsevier B.V. All rights reserved. |
关键词 | Hand segmentation Un-supervised Semi-supervised Deep convolutional neural network Noisy label |
DOI | 10.1016/j.neucom.2018.12.010 |
关键词[WOS] | RECOGNITION ; ALGORITHM ; GESTURE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic Priority Research Program of Chinese Academy of Science[XDB32000000] ; National Natural Science Foundation of China[61702323] ; National Natural Science Foundation of China[61502494] ; National Natural Science Foundation of China[51705515] ; National Natural Science Foundation of China (NSFC)[U1613213] ; National Natural Science Foundation of China (NSFC)[U1713201] ; National Key Research and Development Program of China[2017YFB1300203] ; National Key Research and Development Program of China[2017YFB1300200] ; National Natural Science Foundation of China[61702516] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; National Natural Science Foundation of China[61702516] ; National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China (NSFC)[U1713201] ; National Natural Science Foundation of China (NSFC)[U1613213] ; National Natural Science Foundation of China[51705515] ; National Natural Science Foundation of China[61502494] ; National Natural Science Foundation of China[61702323] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000458626300002 |
出版者 | ELSEVIER SCIENCE BV |
七大方向——子方向分类 | 多智能体系统 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25041 |
专题 | 复杂系统管理与控制国家重点实验室_机器人理论与应用 |
通讯作者 | Qiao, Hong |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China 5.Brunel Univ, Dept Informat Syst & Comp, Uxbridge UB8 3PH, Middx, England 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 7.Univ Sci & Technol Beijing, Sch Mech Engn, Beijing 100083, Peoples R China 8.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
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 | Li, Yinlin,Jia, Lihao,Wang, Zidong,et al. Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning[J]. NEUROCOMPUTING,2019,334(2019):11-24. |
APA | Li, Yinlin,Jia, Lihao,Wang, Zidong,Qian, Yang,&Qiao, Hong.(2019).Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning.NEUROCOMPUTING,334(2019),11-24. |
MLA | Li, Yinlin,et al."Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning".NEUROCOMPUTING 334.2019(2019):11-24. |
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