Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

Zheng-ping Hu; Yi Liu; Xuan Zhang; Yang-hua Yin; Rui-xue Zhang; De-gang Sun

doi:doi:10.11648/j.ijiis.20180702.11

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Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, De-gang Sun

Published in International Journal of Intelligent Information Systems (Volume 7, Issue 2)

Received: 24 May 2018 Accepted: 30 August 2018 Published: 26 September 2018

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Abstract

In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.

Published in	International Journal of Intelligent Information Systems (Volume 7, Issue 2)
DOI	10.11648/j.ijiis.20180702.11
Page(s)	15-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Pattern Recognition, Dictionary Learning, Kernel Space, Collaborative Representation

References

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APA Style

Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. (2018). Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. International Journal of Intelligent Information Systems, 7(2), 15-22. https://doi.org/10.11648/j.ijiis.20180702.11

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ACS Style

Zheng-ping Hu; Yi Liu; Xuan Zhang; Yang-hua Yin; Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int. J. Intell. Inf. Syst. 2018, 7(2), 15-22. doi: 10.11648/j.ijiis.20180702.11

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AMA Style

Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int J Intell Inf Syst. 2018;7(2):15-22. doi: 10.11648/j.ijiis.20180702.11

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@article{10.11648/j.ijiis.20180702.11,
  author = {Zheng-ping Hu and Yi Liu and Xuan Zhang and Yang-hua Yin and Rui-xue Zhang and De-gang Sun},
  title = {Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning},
  journal = {International Journal of Intelligent Information Systems},
  volume = {7},
  number = {2},
  pages = {15-22},
  doi = {10.11648/j.ijiis.20180702.11},
  url = {https://doi.org/10.11648/j.ijiis.20180702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20180702.11},
  abstract = {In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.},
 year = {2018}
}

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TY  - JOUR
T1  - Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning
AU  - Zheng-ping Hu
AU  - Yi Liu
AU  - Xuan Zhang
AU  - Yang-hua Yin
AU  - Rui-xue Zhang
AU  - De-gang Sun
Y1  - 2018/09/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijiis.20180702.11
DO  - 10.11648/j.ijiis.20180702.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 15
EP  - 22
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20180702.11
AB  - In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.
VL  - 7
IS  - 2
ER  -

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Author Information

Zheng-ping Hu

Department of Electronic Information and Engineering, Shandong Huayu University of Technology, Dezhou, China
Yi Liu

Department of Information and Engineering, Yanshan University, Qinhuangdao, China
Xuan Zhang

Department of Information and Engineering, Yanshan University, Qinhuangdao, China
Yang-hua Yin

Department of Information and Engineering, Yanshan University, Qinhuangdao, China
Rui-xue Zhang

Department of Information and Engineering, Yanshan University, Qinhuangdao, China
De-gang Sun

Department of Electronic Information and Engineering, Shandong Huayu University of Technology, Dezhou, China

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Plain Text BibTeX RIS

APA Style

Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. (2018). Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. International Journal of Intelligent Information Systems, 7(2), 15-22. https://doi.org/10.11648/j.ijiis.20180702.11

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ACS Style

Zheng-ping Hu; Yi Liu; Xuan Zhang; Yang-hua Yin; Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int. J. Intell. Inf. Syst. 2018, 7(2), 15-22. doi: 10.11648/j.ijiis.20180702.11

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AMA Style

Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int J Intell Inf Syst. 2018;7(2):15-22. doi: 10.11648/j.ijiis.20180702.11

Copy | Download

@article{10.11648/j.ijiis.20180702.11,
  author = {Zheng-ping Hu and Yi Liu and Xuan Zhang and Yang-hua Yin and Rui-xue Zhang and De-gang Sun},
  title = {Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning},
  journal = {International Journal of Intelligent Information Systems},
  volume = {7},
  number = {2},
  pages = {15-22},
  doi = {10.11648/j.ijiis.20180702.11},
  url = {https://doi.org/10.11648/j.ijiis.20180702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20180702.11},
  abstract = {In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning
AU  - Zheng-ping Hu
AU  - Yi Liu
AU  - Xuan Zhang
AU  - Yang-hua Yin
AU  - Rui-xue Zhang
AU  - De-gang Sun
Y1  - 2018/09/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijiis.20180702.11
DO  - 10.11648/j.ijiis.20180702.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 15
EP  - 22
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20180702.11
AB  - In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.
VL  - 7
IS  - 2
ER  -

Copy | Download