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Semi-supervised Learning for RGB-D Object Recognition
Yanhua Cheng; Xin Zhao; Kaiqi Huang; Tieniu Tan
Conference NameInternational Conference on Pattern Recognition
Source PublicationProc. International Conference on Pattern Recognition 2014
Conference Date2014-08-01
Conference PlaceStockholm, Sweden
AbstractConventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images, (2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively, (2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.
KeywordAccuracy   cameras   feature Extraction   object Recognition
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Yanhua Cheng,Xin Zhao,Kaiqi Huang,et al. Semi-supervised Learning for RGB-D Object Recognition[C],2014:2377-2382.
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