基于深度学习的图像分类研究综述

苏赋; 吕沁; 罗仁泽

doi:10.11959/j.issn.1000-0801.2019268

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基于深度学习的图像分类研究综述

综述 | 更新时间：2024-06-05

- 基于深度学习的图像分类研究综述
- Review of image classification based on deep learning
- 电信科学 2019年35卷第11期页码：58-74
- 作者机构：
  
  1. 西南石油大学电气信息学院，四川成都 610500
  2. 西南石油大学地球科学与技术学院，四川成都 610500
- 作者简介：
  
  [ "苏赋（1973- ），女，博士，西南石油大学副教授，主要研究方向为信号与信息处理" ]
  [ "吕沁（1995- ），女，西南石油大学硕士生，主要研究方向为深度学习与图像处理" ]
  [ "罗仁泽（1973- ），男，博士，西南石油大学教授、博士生导师，主要研究方向为信号处理与人工智能" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program(2016YFC0601100);四川省科技计划基金资助项目;Sichuan Science and Technology Project(2019CXRC0027)
- DOI：10.11959/j.issn.1000-0801.2019268
  中图分类号： TP393
- 网络出版日期：2019-11，
  
  纸质出版日期：2019-11-20
- 稿件说明：
移动端阅览
苏赋, 吕沁, 罗仁泽. 基于深度学习的图像分类研究综述[J]. 电信科学, 2019,35(11):58-74.

Fu SU, Qin LV, Renze LUO. Review of image classification based on deep learning[J]. Telecommunications science, 2019, 35(11): 58-74.
苏赋, 吕沁, 罗仁泽. 基于深度学习的图像分类研究综述[J]. 电信科学, 2019,35(11):58-74. DOI： 10.11959/j.issn.1000-0801.2019268.

Fu SU, Qin LV, Renze LUO. Review of image classification based on deep learning[J]. Telecommunications science, 2019, 35(11): 58-74. DOI： 10.11959/j.issn.1000-0801.2019268.

摘要

近年来，深度学习在计算机视觉领域中的表现优于传统的机器学习技术，而图像分类问题是其中最突出的研究课题之一。传统的图像分类方法难以处理庞大的图像数据，且无法满足人们对图像分类精度和速度的要求，而基于深度学习的图像分类方法突破了此瓶颈，成为目前图像分类的主流方法。从图像分类的研究意义出发，介绍了其发展现状。其次，具体分析了图像分类中最重要的深度学习方法（即自动编码器、深度信念网络与深度玻尔兹曼机）以及卷积神经网络的结构、优点和局限性。再次，对比分析了方法之间的差异及其在常用数据集上的性能表现。最后，探讨了深度学习方法在图像分类领域的不足及未来可能的研究方向。

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.

关键词

Keywords

references

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