Review of Convolutional Neural Network Models and Image Classification

Authors

  • Weiqi Hua
  • Chunzhong Li
  • Xinsheng Wang

DOI:

https://doi.org/10.54097/644jqv20

Keywords:

Convolutional neural network, Image classification, ResNet, ShuffleNet.

Abstract

With the arrival of the era of big data and the improvement of computing power, deep learning has swept the world. Traditional image classification methods are difficult to deal with the huge image data, and cannot meet the requirements of people on the accuracy and speed of image classification, the image classification method based on convolutional neural network breaks through the bottleneck of the traditional image classification method, and becomes the mainstream algorithm of image classification, how to effectively use convolutional neural network to classify images has become a hot spot of research in the field of computer vision at home and abroad. In this paper, we review the research background, significance and current research status of convolutional neural network model and image classification, study two image classification methods based on ResNet and ShuffleNet, and provide a comprehensive review of the construction methods and characteristics of the two deep convolutional neural network model structures, and finally compare and analyse the performance of the two classification models.

Downloads

Download data is not yet available.

References

LeCun Y, Learning invariant feature hierarchies[C]//European conference on computer vision. Springer, Berlin, Heidelberg, 2012: 496-505.

Deng I, Dong W, Socher R, et al. Image Net: A Largescale Hierarchical Image Database[C]//Computer Vision and Pattern Recognition. IEEE Conference on. IEEE, 2009: 248-255.

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017.60(6): 84-90.

LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Pro-ceedings of the IEEE, 1998, 86(11): 2278-2324.

Szegedy C, Wei L, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015, 24(1):205-211.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014. 36(1):231-235.

Larochelle H, Mandel M, Pascanu R, et al. Leaming algorithms for the classification restricted Boltzmann machine[J]. The Journal of Machine Learning Research, 2012, 13(1): 643-669.

He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[J]. IEEE, 2016:770-778.

Zhang X, Zhou X, Lin M, et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices[J]. 2017.

Arandjelovic R, Zisserman A. All about VLAD[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.2013:1578-1585.

Sanchez J, Perronnin F, Mensink T, et al. Image classification with the fisher vector: Theory and practice[J]. International journal of computer vision, 2013, 105(3): 222-245.

Srivastava R K, Greff K, Schmidhuber J. Highway networks[J]. arXiv preprint arXiv:1505.00387,2015.

Downloads

Published

27-04-2024

Issue

Section

Articles

How to Cite

Review of Convolutional Neural Network Models and Image Classification. (2024). Academic Journal of Science and Technology, 10(3), 178-184. https://doi.org/10.54097/644jqv20

Similar Articles

1-10 of 280

You may also start an advanced similarity search for this article.