The Development of Image Classification Models Based on Computer Vision
DOI:
https://doi.org/10.54097/hset.v34i.5505Keywords:
Image Classification; Computer Vision; Deep Learning; Convolutional Neural Network.Abstract
Image classification is a fundamental problem in computer vision, which deserves much attention in the past decade. To provide a comprehensive recall for the image classification task based on the deep learning algorithms, this paper first provides a brief history of computer vision and convolutional neural networks. Then, the current state of research and the direction of development of deep learning-based convolutional neural network models for image classification are examined. In addition, the basic model structure, convolution feature extraction, and pooling operations of standard and convolutional neural networks are also introduced. This paper summarizes the development of convolutional neural network models in recent years. In summary, based on the review of the development of image classification algorithms at home and abroad, the current mainstream image classification algorithms and frontier progress are summarized and analyzed, and the existing problems and future development directions of image classification are summarized and prospected. It can be concluded that deep learning-based methods has a great potential in this case.
Downloads
References
Huang, T., Yang, G. J. T. G. Y., & Tang, G. A fast two-dimensional median filtering algorithm [J]. IEEE transactions on acoustics, speech, and signal processing, 1979, 27(1), 13-18.
Pan, J. J., Tang, Y. Y., & Pan, B. C. The algorithm of fast mean filtering [C]. In 2007 International Conference on Wavelet Analysis and Pattern Recognition (Vol. 1, pp. 244-248), 2007, IEEE.
Kovesi, P. Fast almost-gaussian filtering [C]. In 2010 International Conference on Digital Image Computing: Techniques and Applications (pp. 121-125), 2010 IEEE.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. Understanding of a convolutional neural network [C]. In 2017 international conference on engineering and technology (ICET) (pp. 1-6), 2017, Ieee.
Alakwaa, W., Nassef, M., & Badr, A. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN) [J]. International Journal of Advanced Computer Science and Applications, 2017, 8(8).
Qiu, Y., Wang, J., Jin, Z., Chen, H., Zhang, M., & Guo, L. Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training [J]. Biomedical Signal Processing and Control, 72, 2020, 103323.
Genome Biology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2441470/figure/F6/, 2008.
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. Convolutional neural networks: an overview and application in radiology [J]. Insights into imaging, 9(4), 611-629, 2018.
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. A survey of convolutional neural networks: analysis, applications, and prospects [J]. IEEE transactions on neural networks and learning systems, 2021.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. Gradient-based learning applied to document recognition [C]. Proceedings of the IEEE, 86(11), 2278-2324, 1998.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. Imagenet classification with deep convolutional neural networks [J]. Communications of the ACM, 60(6), 84-90, 2017.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. Going deeper with convolutions [C]. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9), 2015.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
