Research on Image Classification and Recognition Technology Based on Machine Learning

[1] Yang, F., Ma, Z., & Xie, M. (2022). Image classification with parallel kpca-pca network. Computational intelligence(2), 38. Search in Google Scholar

[2] Guo, L. (2022). Sar image classification based on multi-feature fusion decision convolutional neural network. IET image processing(1), 16. Search in Google Scholar

[3] Yang, N., & Zhang, Y. (2022). A gaussian process classification and target recognition algorithm for sar images. Scientific programming(Pt.2), 2022. Search in Google Scholar

[4] Liao, D., Cui, Z., Zhu, Z., Jiang, Z., Zheng, Q., & Wu, N. (2023). A nondestructive recognition and classification method for detecting surface defects of si3n4 bearing balls based on an optimized convolutional neural network. Optical Materials, 136, 113401-. Search in Google Scholar

[5] Lin, C. C., Kuo, C. H., & Chiang, H. T. (2022). Cnn-based classification for point cloud object with bearing angle image. IEEE sensors journal(22-1). Search in Google Scholar

[6] Zhang, X., Liu, J., Zheng, Y., Zheng, Y., & Hussain, M. (2023). A novel shape classification method using 1-d convolutional neural networks. IET image processing. Search in Google Scholar

[7] Osipov, A., Pleshakova, E., Gataullin, S., Korchagin, S., Ivanov, M., & Finogeev, A., et al. (2022). Deep learning method for recognition and classification of images from video recorders in difficult weather conditions. Sustainability, 14. Search in Google Scholar

[8] Wang, H., Huang, D., & Wang, Y. (2022). Gridnet: efficiently learning deep hierarchical representation for 3d point cloud understanding. Frontiers of Computer Science, 16(1), 1–9. Search in Google Scholar

[9] Baraboshkin, E. E., Demidov, A. E., & Orlov, D. M. K. D. A. (2022). Core box image recognition and its improvement with a new augmentation technique. Computers & geosciences, 162(5), 105099.1–105099.10. Search in Google Scholar

[10] Liao, L. (2022). Support vector machine parallelized remote sensing image classification algorithm based on big data. Journal of electronic imaging. Search in Google Scholar

[11] Machado, G. R., Silva, E., & Goldschmidt, R. R. (2023). Adversarial machine learning in image classification: a survey toward the defender’s perspective. ACM computing surveys. Search in Google Scholar

[12] Wu, S., Gao, X., & Hu, X. (2023). Task‐oriented feature hallucination for few‐shot image classification. IET image processing(12), 17. Search in Google Scholar

[13] Vankdothu, R., & Hameed, M. A. (2022). Adaptive features selection and ednn based brain image recognition on the internet of medical things. Computers and Electrical Engineering. Search in Google Scholar

[14] Gangwar, A., Gonzalez-Castro, V., Alegre, E., & Fidalgo, E. (2023). Triple-biggan: semi-supervised generative adversarial networks for image synthesis and classification on sexual facial expression recognition. Neurocomputing. Search in Google Scholar

[15] Das, A., Mohapatra, S. K., & Mohanty, M. N. (2022). Design of deep ensemble classifier with fuzzy decision method for biomedical image classification. Applied Soft Computing(115-), 115. Search in Google Scholar

[16] Zhang, Q., Xu, Y., Zhang, J., & Tao, D. (2023). Vitaev2: vision transformer advanced by exploring inductive bias for image recognition and beyond. International Journal of Computer Vision. Search in Google Scholar

[17] Sogi, N., Zhu, R., Xue, J. H., & Fukui, K. (2022). Constrained mutual convex cone method for image set based recognition. Pattern Recognition, 121, 108190-. Search in Google Scholar