Semi-supervised Learning for RGB-D Object Recognition
文献类型：会议论文
| 作者 | Yanhua Cheng ; Xin Zhao; Kaiqi Huang; Tieniu Tan
|
| 出版日期 | 2014 |
| 会议日期 | 2014-08-01 |
| 会议地点 | Stockholm, Sweden |
| 关键词 | Accuracy cameras feature Extraction object Recognition |
| 页码 | 2377-2382 |
| 英文摘要 | Conventional 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. |
| 会议录 | Proc. International Conference on Pattern Recognition 2014
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12684]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Kaiqi Huang |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Yanhua Cheng,Xin Zhao,Kaiqi Huang,et al. Semi-supervised Learning for RGB-D Object Recognition[C]. 见:. Stockholm, Sweden. 2014-08-01. |
入库方式： OAI收割
来源：自动化研究所
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