Research of Convolutional Neural Network on Image Classification
DOI:
https://doi.org/10.54097/hset.v39i.6656Keywords:
Image Classification Convolutional; Attention Mechanism; Neural Network.Abstract
With the progress of artificial intelligence, technology based on deep learning is becoming more and more mature, and the application of deep convolutional neural network for image classification has become a popular topic for researchers. The number of the structure of deep convolutional neural network for image classification is keep increasing, and its performance is consistently improving, gradually replace that of traditional methods. According to the process of model development and model optimization, this paper divides the convolutional neural network into two models: classical deep convolutional neural network model and attention mechanism deep convolutional neural network model. The construction methods and characteristics of various kinds of deep convolutional neural network models are comprehensively reviewed, and the performance of various classification models is compared and analyzed. Finally, the problems of deep convolutional neural network model are presented.
Downloads
References
Cheng Qiyun, Sun Caixin, Zhang Xiaoxing, et al. Short-Term load forecasting model and method for power system based on complementation of neural network and fuzzy logic. Transactions of China Electrotechnical Society, 2004, 19(10): 53-58.
Zhang K, Feng X H, Guo Y R, Su Y K, Zhao K, Zhao Z B, Ma Z Y and Ding Q L, Overview of deep convolutional neural networks for image classification. Journal of Image and Graphics, 2021,26 (10): 2305-2325.
Yong L I , Lin X Z , Jiang M Y . Facial Expression Recognition with Cross-connect LeNet-5 Network. Acta Automatica Sinica, 2018. 9: 3024-3033.
Ghiasi G, Lin T Y and Le Q V. NAS-FPN: learning scalable feature pyramid architecture for object detection. Proceedings of 2019 IEEE/ CVF Conference on Computer Vision and Pattern Recognition, 2019: 7036-7045.
Zhang K, Guo L R, Gao C and Zhao Z B, Pyramidal RoR for image classification. Cluster Computing, 2019, 22(2): 5115-5125.
Guo Y R, Zhang K, Wang X S, Yuan J S, Zhao Z B and Ma Z Y, Image classification method based on end-to-end dual feature reweight DenseNet. Journal of Image and Graphics, 2020, 25 (3): 486- 497.
Kajkamhaeng S, Chantrapornchai C. SE-SqueezeNet: SqueezeNet extension with squeeze-and-excitation block. International Journal of Computational Science and Engineering, 2021(2):24.
Wang F, Jiang M Q, Qian C, Yang S, Li C, Zhang H G, Wang X G and Tang X O, Residual attention network for image classification. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2017:6450- 6458.
Yang Y B, Zhong Z S, Shen T C and Lin Z C, Convolutional neural networks with alternately updated clique, Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018: 2413-2422.
Szegedy C, Ioffe S, Vanhoucke V and Alemi A A, Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, 2016: 4278-428.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
