Volume 7, Issue 4, August 2019, Page: 99-110
Layered Feature Recognition Algorithm Based on Combined Convolution
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
Received: Nov. 19, 2019;       Accepted: Dec. 11, 2019;       Published: Dec. 24, 2019
DOI: 10.11648/j.acis.20190704.11      View  253      Downloads  94
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.
Keywords
Pattern Recognition, Convolution Algorithm, Single Sample, Face Recognition, Handwritten Digital Recognition
To cite this article
Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, Chenxin Ma, Wenhao Zhou, Layered Feature Recognition Algorithm Based on Combined Convolution, Automation, Control and Intelligent Systems. Vol. 7, No. 4, 2019, pp. 99-110. doi: 10.11648/j.acis.20190704.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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