Layered Feature Recognition Algorithm Based on Combined Convolution

Shuduo Zhao; Xu Han; Jin Xu; Haiyun Chen; Guanqin Feng; Chenxin Ma; Wenhao Zhou

doi:doi:10.11648/j.acis.20190704.11

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Layered Feature Recognition Algorithm Based on Combined Convolution

Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, Chenxin Ma, Wenhao Zhou

Published in Automation, Control and Intelligent Systems (Volume 7, Issue 4)

Received: 19 November 2019 Accepted: 11 December 2019 Published: 24 December 2019

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Abstract

In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.

Published in	Automation, Control and Intelligent Systems (Volume 7, Issue 4)
DOI	10.11648/j.acis.20190704.11
Page(s)	99-110
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), 2019. Published by Science Publishing Group

Keywords

Pattern Recognition, Convolution Algorithm, Single Sample, Face Recognition, Handwritten Digital Recognition

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APA Style

Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. (2019). Layered Feature Recognition Algorithm Based on Combined Convolution. Automation, Control and Intelligent Systems, 7(4), 99-110. https://doi.org/10.11648/j.acis.20190704.11

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ACS Style

Shuduo Zhao; Xu Han; Jin Xu; Haiyun Chen; Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom. Control Intell. Syst. 2019, 7(4), 99-110. doi: 10.11648/j.acis.20190704.11

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AMA Style

Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom Control Intell Syst. 2019;7(4):99-110. doi: 10.11648/j.acis.20190704.11

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@article{10.11648/j.acis.20190704.11,
  author = {Shuduo Zhao and Xu Han and Jin Xu and Haiyun Chen and Guanqin Feng and Chenxin Ma and Wenhao Zhou},
  title = {Layered Feature Recognition Algorithm Based on Combined Convolution},
  journal = {Automation, Control and Intelligent Systems},
  volume = {7},
  number = {4},
  pages = {99-110},
  doi = {10.11648/j.acis.20190704.11},
  url = {https://doi.org/10.11648/j.acis.20190704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20190704.11},
  abstract = {In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.},
 year = {2019}
}

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TY  - JOUR
T1  - Layered Feature Recognition Algorithm Based on Combined Convolution
AU  - Shuduo Zhao
AU  - Xu Han
AU  - Jin Xu
AU  - Haiyun Chen
AU  - Guanqin Feng
AU  - Chenxin Ma
AU  - Wenhao Zhou
Y1  - 2019/12/24
PY  - 2019
N1  - https://doi.org/10.11648/j.acis.20190704.11
DO  - 10.11648/j.acis.20190704.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 99
EP  - 110
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20190704.11
AB  - In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.
VL  - 7
IS  - 4
ER  -

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Author Information

Shuduo Zhao

School of Information, Southwest Petroleum University, Nanchong, China
Xu Han

School of Information, Southwest Petroleum University, Nanchong, China
Jin Xu

School of Information, Southwest Petroleum University, Nanchong, China
Haiyun Chen

School of Information, Southwest Petroleum University, Nanchong, China
Guanqin Feng

School of Information, Southwest Petroleum University, Nanchong, China
Chenxin Ma

School of Information, Southwest Petroleum University, Nanchong, China
Wenhao Zhou

School of Information, Southwest Petroleum University, Nanchong, China

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Plain Text BibTeX RIS

APA Style

Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. (2019). Layered Feature Recognition Algorithm Based on Combined Convolution. Automation, Control and Intelligent Systems, 7(4), 99-110. https://doi.org/10.11648/j.acis.20190704.11

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ACS Style

Shuduo Zhao; Xu Han; Jin Xu; Haiyun Chen; Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom. Control Intell. Syst. 2019, 7(4), 99-110. doi: 10.11648/j.acis.20190704.11

Copy | Download

AMA Style

Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom Control Intell Syst. 2019;7(4):99-110. doi: 10.11648/j.acis.20190704.11

Copy | Download

@article{10.11648/j.acis.20190704.11,
  author = {Shuduo Zhao and Xu Han and Jin Xu and Haiyun Chen and Guanqin Feng and Chenxin Ma and Wenhao Zhou},
  title = {Layered Feature Recognition Algorithm Based on Combined Convolution},
  journal = {Automation, Control and Intelligent Systems},
  volume = {7},
  number = {4},
  pages = {99-110},
  doi = {10.11648/j.acis.20190704.11},
  url = {https://doi.org/10.11648/j.acis.20190704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20190704.11},
  abstract = {In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Layered Feature Recognition Algorithm Based on Combined Convolution
AU  - Shuduo Zhao
AU  - Xu Han
AU  - Jin Xu
AU  - Haiyun Chen
AU  - Guanqin Feng
AU  - Chenxin Ma
AU  - Wenhao Zhou
Y1  - 2019/12/24
PY  - 2019
N1  - https://doi.org/10.11648/j.acis.20190704.11
DO  - 10.11648/j.acis.20190704.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 99
EP  - 110
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20190704.11
AB  - In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.
VL  - 7
IS  - 4
ER  -

Copy | Download