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- Publisher Website: 10.1109/TIP.2012.2215620
- Scopus: eid_2-s2.0-84872241132
- PMID: 23014744
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Article: Sparse representation with kernels
Title | Sparse representation with kernels |
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Authors | |
Keywords | Face recognition image classification kernel matrix approximation kernel sparse representation nonlinear mapping sparse coding |
Issue Date | 2013 |
Citation | IEEE Transactions on Image Processing, 2013, v. 22, n. 2, p. 423-434 How to Cite? |
Abstract | Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. © 1992-2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/345202 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
DC Field | Value | Language |
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dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Tsang, Ivor Wai Hung | - |
dc.contributor.author | Chia, Liang Tien | - |
dc.date.accessioned | 2024-08-15T09:25:52Z | - |
dc.date.available | 2024-08-15T09:25:52Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2013, v. 22, n. 2, p. 423-434 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345202 | - |
dc.description.abstract | Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. © 1992-2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | Face recognition | - |
dc.subject | image classification | - |
dc.subject | kernel matrix approximation | - |
dc.subject | kernel sparse representation | - |
dc.subject | nonlinear mapping | - |
dc.subject | sparse coding | - |
dc.title | Sparse representation with kernels | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2012.2215620 | - |
dc.identifier.pmid | 23014744 | - |
dc.identifier.scopus | eid_2-s2.0-84872241132 | - |
dc.identifier.volume | 22 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 423 | - |
dc.identifier.epage | 434 | - |