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Conference Paper: SCaLE: Supervised and cascaded laplacian eigenmaps for visual object recognition based on nearest neighbors
Title | SCaLE: Supervised and cascaded laplacian eigenmaps for visual object recognition based on nearest neighbors |
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Authors | |
Issue Date | 2013 |
Publisher | IEEE Computer Society. |
Citation | The 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), Portland, OR., 23-28 June 2013. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2013, p. 867-874 How to Cite? |
Abstract | Recognizing the category of a visual object remains a challenging computer vision problem. In this paper we develop a novel deep learning method that facilitates example-based visual object category recognition. Our deep learning architecture consists of multiple stacked layers and computes an intermediate representation that can be fed to a nearest-neighbor classifier. This intermediate representation is discriminative and structure-preserving. It is also capable of extracting essential characteristics shared by objects in the same category while filtering out nonessential differences among them. Each layer in our model is a nonlinear mapping, whose parameters are learned through two sequential steps that are designed to achieve the aforementioned properties. The first step computes a discrete mapping called supervised Laplacian Eigenmap. The second step computes a continuous mapping from the discrete version through nonlinear regression. We have extensively tested our method and it achieves state-of-the-art recognition rates on a number of benchmark datasets. |
Description | This CVPR 2013 paper is the Open Access version, provided by the Computer Vision Foundation. The authoritative version of this paper is available in IEEE Xplore (NYP). |
Persistent Identifier | http://hdl.handle.net/10722/186496 |
ISSN | 2023 SCImago Journal Rankings: 10.331 |
DC Field | Value | Language |
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dc.contributor.author | Wu, R | en_US |
dc.contributor.author | Yu, Y | en_US |
dc.contributor.author | Wang, WP | en_US |
dc.date.accessioned | 2013-08-20T12:11:14Z | - |
dc.date.available | 2013-08-20T12:11:14Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), Portland, OR., 23-28 June 2013. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2013, p. 867-874 | en_US |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/186496 | - |
dc.description | This CVPR 2013 paper is the Open Access version, provided by the Computer Vision Foundation. The authoritative version of this paper is available in IEEE Xplore (NYP). | - |
dc.description.abstract | Recognizing the category of a visual object remains a challenging computer vision problem. In this paper we develop a novel deep learning method that facilitates example-based visual object category recognition. Our deep learning architecture consists of multiple stacked layers and computes an intermediate representation that can be fed to a nearest-neighbor classifier. This intermediate representation is discriminative and structure-preserving. It is also capable of extracting essential characteristics shared by objects in the same category while filtering out nonessential differences among them. Each layer in our model is a nonlinear mapping, whose parameters are learned through two sequential steps that are designed to achieve the aforementioned properties. The first step computes a discrete mapping called supervised Laplacian Eigenmap. The second step computes a continuous mapping from the discrete version through nonlinear regression. We have extensively tested our method and it achieves state-of-the-art recognition rates on a number of benchmark datasets. | - |
dc.language | eng | en_US |
dc.publisher | IEEE Computer Society. | - |
dc.relation.ispartof | IEEE Conference on Computer Vision and Pattern Recognition Proceedings | en_US |
dc.rights | IEEE Conference on Computer Vision and Pattern Recognition Proceedings. Copyright © IEEE Computer Society. | - |
dc.rights | ©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.title | SCaLE: Supervised and cascaded laplacian eigenmaps for visual object recognition based on nearest neighbors | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | WU, R: rbwu@cs.hku.hk | en_US |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | en_US |
dc.identifier.email | Wang, WP: wenping@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | en_US |
dc.identifier.authority | Wang, WP=rp00186 | en_US |
dc.description.nature | postprint | - |
dc.identifier.hkuros | 220948 | en_US |
dc.identifier.spage | 867 | - |
dc.identifier.epage | 874 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 130830 | - |
dc.identifier.issnl | 1063-6919 | - |