Image Classification and Recognition

Image classification and recognition are critical components of computer vision, allowing machines to analyse and comprehend visual data. These capabilities have numerous applications in various fields, including healthcare, security, transportation, and entertainment. Recent advances in deep learning techniques have significantly improved the accuracy and efficiency of image classification and recognition. This paper aims to provide a comprehensive overview of the state-of-the-art in this field, reviewing the latest research and methodologies used for image classification and recognition. Our analysis reveals that deep learning models, particularly convolution neural networks (CNNs), have proven highly effective for this task, achieving impressive results on large-scale datasets. However, challenges remain in improving the robustness of these models and addressing issues such as bias and lack of diversity in training data.

[1]. Deng, J., Dong, W., Socher, R., Li, L., Kai, L., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248-255). IEEE.

[2]. 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).

[3]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

[4]. Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., … & Summers, R. M. (2016). Deep CNNfor computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5), 1285-1298.

[5]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[6]. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248-255). IEEE.

[7]. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … & Berg, A. C. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.

[8]. 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).

[9]. 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).

[10]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).

[11]. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.

[12]. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).

Ramji Rajbhar, C. Kalpana, “Image Classification and Recognition”, REST Journal on Data Analytics and Artificial Intelligence, 2(2), June 2023:74-78.