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- Publisher Website: 10.1109/TMM.2015.2476660
- Scopus: eid_2-s2.0-84946732106
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Article: RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge
Title | RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge |
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Authors | |
Keywords | Deep learning RGB-D object recognition Transfer learning |
Issue Date | 2015 |
Citation | IEEE Transactions on Multimedia, 2015, v. 17, n. 11, p. 1899-1908 How to Cite? |
Abstract | For the task of RGB-D object recognition, it is important to identify suitable representations of images, which can boost the performance of object recognition. In this work, we propose a novel representation learning method for RGB-D images by jointly incorporating the underlying data structure and the prior knowledge of the data. Specifically, the convolutional neural networks (CNN) are employed to learn image representation by exploiting the underlying data structure. To handle the problem of the limited RGB and depth images for object recognition, the multi-level hierarchies of features trained on ImageNet from the CNN are transferred to learn rich generic feature representation for RGB and depth images while the labeled images are leveraged. On the other hand, we propose a novel deep auto-encoders (DAE) to exploit the prior knowledge, which can overcome the expensive computational cost of optimization in feature encoding. The expected representations of images are obtained by integrating the two types of image representations. To verify the effectiveness of the proposed method, we thoroughly conduct extensive experiments on two publicly available RGB-D datasets. The encouraging experimental results compared with the state-of-the-art approaches demonstrate the advantages of the proposed method. |
Persistent Identifier | http://hdl.handle.net/10722/345082 |
ISSN | 2023 Impact Factor: 8.4 2023 SCImago Journal Rankings: 2.260 |
DC Field | Value | Language |
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dc.contributor.author | Tang, Jinhui | - |
dc.contributor.author | Jin, Lu | - |
dc.contributor.author | Li, Zechao | - |
dc.contributor.author | Gao, Shenghua | - |
dc.date.accessioned | 2024-08-15T09:25:07Z | - |
dc.date.available | 2024-08-15T09:25:07Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Multimedia, 2015, v. 17, n. 11, p. 1899-1908 | - |
dc.identifier.issn | 1520-9210 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345082 | - |
dc.description.abstract | For the task of RGB-D object recognition, it is important to identify suitable representations of images, which can boost the performance of object recognition. In this work, we propose a novel representation learning method for RGB-D images by jointly incorporating the underlying data structure and the prior knowledge of the data. Specifically, the convolutional neural networks (CNN) are employed to learn image representation by exploiting the underlying data structure. To handle the problem of the limited RGB and depth images for object recognition, the multi-level hierarchies of features trained on ImageNet from the CNN are transferred to learn rich generic feature representation for RGB and depth images while the labeled images are leveraged. On the other hand, we propose a novel deep auto-encoders (DAE) to exploit the prior knowledge, which can overcome the expensive computational cost of optimization in feature encoding. The expected representations of images are obtained by integrating the two types of image representations. To verify the effectiveness of the proposed method, we thoroughly conduct extensive experiments on two publicly available RGB-D datasets. The encouraging experimental results compared with the state-of-the-art approaches demonstrate the advantages of the proposed method. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Multimedia | - |
dc.subject | Deep learning | - |
dc.subject | RGB-D object recognition | - |
dc.subject | Transfer learning | - |
dc.title | RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TMM.2015.2476660 | - |
dc.identifier.scopus | eid_2-s2.0-84946732106 | - |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 1899 | - |
dc.identifier.epage | 1908 | - |