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Semi-supervised learning and feature evaluation for RGB-D object recognition
Cheng, Yanhua; Zhao, Xin; Huang, Kaiqi; Tan, Tieniu
AbstractWith 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.
KeywordRgb-d Object Recognition Feature Representation Feature Evaluation Semi-supervised Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000361081600012
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Document Type期刊论文
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
Recommended Citation
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(5):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(5),149-160.
MLA Cheng, Yanhua,et al."Semi-supervised learning and feature evaluation for RGB-D object recognition".COMPUTER VISION AND IMAGE UNDERSTANDING 139.5(2015):149-160.
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