基于深度学习的图像分类研究综述

doi:10.11959/j.issn.1000-0801.2019268

Abstract

Abstract:

In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.

Key words: deep learning, image classification, auto-encoders, deep belief networks, CNN

CLC Number:

TP393

Fu SU,Qin LV,Renze LUO. Review of image classification based on deep learning[J]. Telecommunications Science, 2019, 35(11): 58-74.

Figures/Tables 15

References 86

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深度学习模型	错误率
KNN^[13]	5.0%
MLP^[18]	2.45%
传统自编码器^[41]	1.78%
堆叠去噪自编码器^[38]	1.28%
DBN^[25]	1.25%
LeNet-5^[18]	0.8%

CNN模型	Top-1错误率（val）	Top-5错误率（val）	Top-5错误率（test）
AlexNet^[74]	36.7%	15.4%	15.3%
VGGNet^[75]	—	8.43%	7.32%
GoogleNet^[78]	—	7.89%	6.66%
BN-Inception^[73]	21.99%	5.82%	—
ResNet-152^[76]	19.38%	4.49%	3.57%
Inception-V3^[79]	18.77%	4.2%	—
DensNet-264^[80]	20.80%	5.29%	—
Attention-92^[84]	19.5%	4.8%	—
1.0 MobileNet-224^[82]	29.4%	—	—
SENet-154^[85]	18.68%	4.47%	2.25%

CNN模型	FLOPS （每秒浮点运算次数）	参数数量/个
LeNet-5^[18]	4×10⁵	6×10⁴
AlexNet^[74]	7×10⁹	6×10⁷
VGGNet-16^[75]	1.5×10¹¹	1.38×10⁸
GoogleNet^[78]	1.5×10¹⁰	5×10⁶
ResNet-50^[76]	3.86×10⁹	2.5×10⁷
Attention-92^[84]	1.04×10¹⁰	5.13×10⁷
MobileNet^[82]	5.69×10⁸	4.2×10⁶
SENet-50^[85]	3.87×10⁹	2.75×10⁷

Review of image classification based on deep learning

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