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[1]张 斌,王 强.一种改进型卷积神经网络的图像分类方法[J].成都信息工程大学学报,2019,(01):39-43.[doi:10.16836/j.cnki.jcuit.2019.01.009]
　ZHANG Bin,WANG Qiang.An Improved Convolution Neural Network Image Classification Method[J].Journal of Chengdu University of Information Technology,2019,(01):39-43.[doi:10.16836/j.cnki.jcuit.2019.01.009]
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一种改进型卷积神经网络的图像分类方法

成都信息工程大学学报[ISSN:1006-6977/CN:61-1281/TN] 卷: 期数: 2019年01期 页码: 39-43 栏目: 电子信息科学与技术 出版日期: 2019-01-15
Title:
An Improved Convolution Neural Network Image Classification Method
文章编号:
2096-1618(2019)01-0039-05
作者:
张 斌1;  王 强2
(1.成都信息工程大学计算机学院,四川 成都 610225; 2.国网四川省电力公司德阳供电公司,四川 德阳 618000)
Author(s):
ZHANG Bin1;  WANG Qiang2
(1.College of Computer Science,Chengdu University of Information Technology,Chengdu 610225,China; 2.State Grid Sichuan Electric Power Company Deyang Power Supply Company,Deyang 618000,China)
关键词:
深度学习;  卷积神经网络;  图像分类;  激活函数
Keywords:
eep learning;  Convolutional Neural Network;  image classification;  activation function
分类号:
TP301.6
DOI:
10.16836/j.cnki.jcuit.2019.01.009
文献标志码:
A
摘要:
基于Keras深度学习框架和卷积层取反操作,提出一种改进型的卷积神经网络结构,网络结构首层采用卷积层取反以增加有效特征信息的传递,有效结合Leaky ReLU激活函数传递至下一层,最后采用Softmax分类器实现图像分类。在两个公共数据集上,同传统的卷积神经网络模型做对比实验,实验结果表明,改进的卷积网络模型是有效的。
Abstract:
Based on Keras deep learning framework and convolution layer inverse operation,an improved Convolutional Neural Network structure is proposed in this paper. The first layer of the network structure uses convolutional layer inversion to increase the transmission of effective feature information.The LeakyReLU activation function is effectively combined to the next layer.Finally, the Softmax classifier is used to implement image classification. Compared with the traditional Convolution Neural Network model on two common datasets, the experimental results show that the improved convolution network model in this paper is effective.

参考文献/References:

[1] Oh J,Lee S,Lee E.A User Modeling Using Implicit Feedback for Effective Recommender System[C].International Conference on Convergence and Hybrid Information Technology.IEEE,2008:55-158.
[2] Boureau Y L,Roux N L,Bach F,et al.Ask the locals:Multi-way local pooling for image recognition[J].IEEE 2011 IEEE International Conference on Computer Vision(ICCV),2011,58(11):2651-2658.
[3] 郎波,黄静,危辉.利用多层视觉网络模型进行图像局部特征表征的方法[J].计算机辅助设计与图形学学报,2015,27(4):703-712.
[4] Le Cun,Y Bengio,G E Hinton,Deep learning[J].Nature,2015,521(7553):436-444.
[5] Hinton G,Deng L,Dong Y,et al.Deep neural networks for acoustic modeling in speech recognition:The shared views of four research groups[J].IEEE Signal Processing Magazine,2012,29(6):82-97.
[6] 吕刚,郝平,盛建荣.一种改进的深度神经网络在小图像分类中的应用研究[J].计算机应用与软件,2014,31(4):182-184.
[7] Ren S,He K,Grishick R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[8] Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C].International Conference on Neural Information Processing Systems,2012:1097-1105.
[9] Hinton G E.A practical guide to training restricted boltzmann machines[M].Springer Berlin Heidelberg,2012:599-619.
[10] Zhang J,Shan S,Kan M,et al.Coarse-to-fine auto-encoder networks for real-time face alignment[C].European Conference on Computer Vision,2014:1-16.
[11] 王强,李孝杰,陈俊.Supplement卷积神经网络的图像分类方法[J].计算机辅助设计与图形学学报,2018(3).
[12] Abadi M,Agarwal A,Barham P,et al.Tensorflow:Large-scale machine learning on heterogeneous distributed systems[J].arXiv preprint arXiv,2016.
[13] Kingma D P,Ba J.Adam:A Method for Stochastic Optimization[J].Computer Science,2014.

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备注/Memo

收稿日期:2018-06-22

更新日期/Last Update: 2019-01-15