Chechik, G., Sharma, V., Shalit, U., & Bengio, S. (2010). Large scale online learning of image similarity through ranking. Journal of Machine Learning Research, 11, 1109-1135. https://doi.org/10.1007/978-3-642-02172-5_2
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. International Conference on Machine Learning.
Guyon, I., & Elisseeff, A. (2006). An introduction to feature extraction. In Feature Extraction: Foundations and Applications (pp. 1-25). Springer.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
Hirematch, P. S., & Puijari, J. (2007). Content-based image retrieval based on color, texture, and shape feature using image and its complement. International Journal of Computer Science and Security, 1(4), 25-35.
Khan, A. S., & Shafique, M. (2018). Content-based image retrieval using histogram of oriented gradients and SIFT features. International Journal of Computer Applications, 179(26), 1-8. https://doi.org/10.5120/ijca2018917508
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
Li, X., & Qian, Y. (2019). A novel image similarity measurement method based on deep learning. Sensors, 19(14), 3060. https://doi.org/10.3390/s19143060
Lu, J., Ma, C. X., Zhou, Y. R., Luo, M. X., & Zhang, K. B.. (2019). Multi-feature fusion for enhancing image similarity learning. IEEE Access, 7, 167547-167556. https://doi.org/10.1109/ACCESS.2019.2953078
Luu, T. H., Phuc, P. N. K., Yu, Z., Pham, D. D., & Cao, H. T. (2022). Face Mask Recognition for Covid-19 Prevention. Computers, Materials & Continua, 73(2). https://doi.org/10.32604/cmc.2022.029663
Luu, T., Ky Phuc, P., Lam, T., Yu, Z., & Lam, V. (2023). Ensembling techniques in solar panel quality classification. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5674-5680. doi:http://doi.org/10.11591/ijece.v13i5.pp5674-5680
McCorduck, P. (2004). Machines who think (2nd ed.). AK Peters. https://doi.org/10.1201/9780429258985
Russakoff, D. B., Tomasi, C., Rohlfing, T., & Maurer, C. R. (2004). Image similarity using mutual information of regions. In European Conference on Computer Vision (pp. 596-607). Springer.
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. https://doi.org/10.1007/978-3-540-24672-5_47
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.,& Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105-6114).
Zagoruyko, S. (2016). Wide residual networks. arXiv preprint arXiv:1605.07146.
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., Yuan, J., & Yan, L. Li. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021(1), 8812542.
