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Semi-supervised learning and feature evaluation for RGB-D object recognition

文献类型：期刊论文

作者Cheng, Yanhua; Zhao, Xin; Huang, Kaiqi; Tan, Tieniu
刊名COMPUTER VISION AND IMAGE UNDERSTANDING
出版日期2015-10-01
卷号139页码:149-160
关键词RGB-D Object recognition Feature representation Feature evaluation Semi-supervised learning
英文摘要With new depth sensing technology such as Kinect providing high quality synchronized RGB and depth images (RGB-D data), combining the two distinct views for object recognition has attracted great interest in computer vision and robotics community. Recent methods mostly employ supervised learning methods for this new RGB-D modality based on the two feature sets. However, supervised learning methods always depend on large amount of manually labeled data for training models. To address the problem, this paper proposes a semi-supervised learning method to reduce the dependence on large annotated training sets. The method can effectively learn from relatively plentiful unlabeled data, if powerful feature representations for both the RGB and depth view can be extracted. Thus, a novel and effective feature termed CNN-SPM-RNN is proposed in this paper, and four representative features (KDES [1], CKM [2], HMP [3] and CNN-RNN [4]) are evaluated and compared with ours under the unified semi-supervised learning framework. Finally, we verify our method on three popular and publicly available RGB-D object databases. The experimental results demonstrate that, with only 20% labeled training set, the proposed method can achieve competitive performance compared with the state of the arts on most of the databases. (C) 2015 Elsevier Inc. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
收录类别SCI
语种英语
WOS记录号WOS:000361081600012
公开日期2015-12-24
源URL[http://ir.ia.ac.cn/handle/173211/8980]  
专题自动化研究所_智能感知与计算研究中心
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Cheng, Yanhua,Zhao, Xin,Huang, Kaiqi,et al. Semi-supervised learning and feature evaluation for RGB-D object recognition[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2015,139:149-160.
APA Cheng, Yanhua,Zhao, Xin,Huang, Kaiqi,&Tan, Tieniu.(2015).Semi-supervised learning and feature evaluation for RGB-D object recognition.COMPUTER VISION AND IMAGE UNDERSTANDING,139,149-160.
MLA Cheng, Yanhua,et al."Semi-supervised learning and feature evaluation for RGB-D object recognition".COMPUTER VISION AND IMAGE UNDERSTANDING 139(2015):149-160.

入库方式： OAI收割

来源：自动化研究所

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