Modality-specific and hierarchical feature learning for RGB-D hand-held object recognition | |
Lv, Xiong2; Liu, Xinda3; Li, Xiangyang2; Li, Xue2,4; Jiang, Shuqiang2; He, Zhiqiang1 | |
刊名 | MULTIMEDIA TOOLS AND APPLICATIONS
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2017-02-01 | |
卷号 | 76期号:3页码:4273-4290 |
关键词 | Feature learning RGB-D object recogntion Multiple modalities |
ISSN号 | 1380-7501 |
DOI | 10.1007/s11042-016-3375-5 |
英文摘要 | Hand-held object recognition is an important research topic in image understanding and plays an essential role in human-machine interaction. With the easily available RGB-D devices, the depth information greatly promotes the performance of object segmentation and provides additional channel information. While how to extract a representative and discriminating feature from object region and efficiently take advantage of the depth information plays an important role in improving hand-held object recognition accuracy and eventual human-machine interaction experience. In this paper, we focus on a special but important area called RGB-D hand-held object recognition and propose a hierarchical feature learning framework for this task. First, our framework learns modality-specific features from RGB and depth images using CNN architectures with different network depth and learning strategies. Secondly a high-level feature learning network is implemented for a comprehensive feature representation. Different with previous works on feature learning and representation, the hierarchical learning method can sufficiently dig out the characteristics of different modal information and efficiently fuse them in a unified framework. The experimental results on HOD dataset illustrate the effectiveness of our proposed method. |
资助项目 | National Basic Research 973 Program of China[2012CB316400] ; National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61322212] ; National High Technology Research and Development 863 Program of China[2014AA015202] ; Lenovo Outstanding Young Scientists Program (LOYS) |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | SPRINGER |
WOS记录号 | WOS:000396051200054 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/7478] ![]() |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | He, Zhiqiang |
作者单位 | 1.Lenovo Corp Res, Beijing 100085, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Ningxia Univ, Sch Math & Comp Sci, Ningxia 750021, Peoples R China 4.Shandong Univ Sci & Technol, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China |
推荐引用方式 GB/T 7714 | Lv, Xiong,Liu, Xinda,Li, Xiangyang,et al. Modality-specific and hierarchical feature learning for RGB-D hand-held object recognition[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(3):4273-4290. |
APA | Lv, Xiong,Liu, Xinda,Li, Xiangyang,Li, Xue,Jiang, Shuqiang,&He, Zhiqiang.(2017).Modality-specific and hierarchical feature learning for RGB-D hand-held object recognition.MULTIMEDIA TOOLS AND APPLICATIONS,76(3),4273-4290. |
MLA | Lv, Xiong,et al."Modality-specific and hierarchical feature learning for RGB-D hand-held object recognition".MULTIMEDIA TOOLS AND APPLICATIONS 76.3(2017):4273-4290. |
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