A new method of image recognition based on deep learning generates adversarial networks and integrates traditional algorithms

Authors

  • Yutian Yang
  • Zexi Chen
  • Yafeng Yan
  • Muqing Li
  • Tana Gegen

DOI:

https://doi.org/10.54097/qnq8g0tj

Keywords:

SVM, GAN, Kernel function

Abstract

This paper discusses an innovative image recognition method, which combines the advanced feature learning ability of generative adversarial networks (Gan) with the robustness of traditional image recognition algorithms. Based on small-sample training, Gan can generate high-quality image samples to expand the training set. Therefore, by designing and training a generative adversarial network, the real image data and its labels can be integrated into the training set. The GAN-generated images, together with corresponding labels, are input into SVM for training, and appropriate kernel functions and parameters are selected to optimize the SVM model to maximize classification performance. GAN is good at data generation and feature learning, while SVM is good at problems with clear classification boundaries, and this model combining the two methods is used in image recognition.

References

Xu, T., Li, I., Zhan, Q., Hu, Y., & Yang, H. (2024). Research on Intelligent System of Multimodal Deep Learning in Image Recognition. Journal of Computing and Electronic Information Management, 12(3), 79-83.

Zhang, H., Diao, S., Yang, Y., Zhong, J., & Yan, Y. (2024). Multi-scale image recognition strategy based on convolutional neural network. Journal of Computing and Electronic Information Management, 12(3), 107-113.

Wang, S., Liu, Z., & Peng, B. (2023, December). A Self-training Framework for Automated Medical Report Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 16443-16449).

Xiao, M., Li, Y., Yan, X., Gao, M., & Wang, W. (2024). Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example. arXiv preprint arXiv:2404.08279.

Dai, W., Tao, J., Yan, X., Feng, Z., & Chen, J. (2023, November). Addressing Unintended Bias in Toxicity Detection: An LSTM and Attention-Based Approach. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 375-379). IEEE.

Mei, T., Zi, Y., Cheng, X., Gao, Z., Wang, Q., & Yang, H. (2024). Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks. arXiv preprint arXiv:2405.11704.

Lu, S., Liu, Z., Liu, T., & Zhou, W. (2023). Scaling-up medical vision-and-language representation learning with federated learning. Engineering Applications of Artificial Intelligence, 126, 107037.

Liu, Z., & Song, J. (2021, November). Comparison of Tree-based Feature Selection Algorithms on Biological Omics Dataset. In Proceedings of the 5th International Conference on Advances in Artificial Intelligence (pp. 165-169).

Zhao, B., Cao, Z., & Wang, S. (2017). Lung vessel segmentation based on random forests. Electronics Letters, 53(4), 220-222.

Yan, X., Wang, W., Xiao, M., Li, Y., & Gao, M. (2024). Survival Prediction Across Diverse Cancer Types Using Neural Networks. arXiv preprint arXiv:2404.08713.

Wang, Q., Schindler, S. E., Chen, G., Mckay, N. S., McCullough, A., Flores, S., ... & Benzinger, T. L. (2024). Investigating White Matter Neuroinflammation in Alzheimer Disease Using Diffusion-Based Neuroinflammation Imaging. Neurology, 102(4), e208013.

Li, Y., Yan, X., Xiao, M., Wang, W., & Zhang, F. (2024). Investigation of Creating Accessibility Linked Data Based on Publicly Available Accessibility Datasets. In Proceedings of the 2023 13th International Conference on Communication and Network Security (pp. 77–81). Association for Computing Machinery.

Yao, J., Wu, T., & Zhang, X. (2023). Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn. arXiv preprint arXiv:2308.08333.

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Published

27-05-2024

Issue

Section

Articles

How to Cite

Yang, Y., Chen, Z., Yan, Y., Li, M., & Gegen, T. (2024). A new method of image recognition based on deep learning generates adversarial networks and integrates traditional algorithms. Journal of Computing and Electronic Information Management, 13(1), 57-61. https://doi.org/10.54097/qnq8g0tj