A Multi-Scale Feature Attention Image Recognition Algorithm

Main Article Content

Xin MingYuan
Ang Ling Weay
Sellappan Palaniappan

Abstract

The success of image classification using small samples is contingent on neural network models' capability to derive image representations from the data. A proposed solution is a small-sample image classification system that leverages attention mechanisms and meta-learning to capture more comprehensive image information. Due to its ability to efficiently suppress irrelevant characteristics and accentuate pertinent ones, this technique may extract more robust multiscale features and enhance classification performance through meta-learning.In this paper, the effectiveness of the multi-scale attention network is verified on two datasets, namely, Mini-ImageNet and Tiered-ImageNet, and the accuracy of the method is 58.54% for 5-way 1shot and 74.76% for 5-way 5shot on the Mini-ImageNet dataset. In the dataset of the Tiered-ImageNet,the accuracy of 5-way 1-shot and 5-way 5-shot increased to 59.74% and 78.65%, respectively. The experimental results show that the multi-scale sub-attention can pay more attention to the global information of the image than the single-scale attention network, and significantly improve the accuracy of small-sample image classification.

Article Details

How to Cite
MingYuan, X., Ling Weay, . A., & Palaniappan, S. (2023). A Multi-Scale Feature Attention Image Recognition Algorithm. Journal of Informatics and Web Engineering, 2(2), 1–7. https://doi.org/10.33093/jiwe.2023.2.2.1
Section
Regular issue

References

A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, International Conference on Neural Information Processing Systems, pp. 1097, 2012. https://doi.org/10.1145/3065386

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, pp. 211-252, 2015. https://doi.org/10.1007/s11263-015-0816-y

Y. Wang, Q. Yao, J. T. Kwok, L. M. Ni, “Generalizing from a Few Examples: A Survey on Few-Shot Learning”, ACM Computing Survey, vol. 53, no. 3, pp 1–34, 2020. https://doi.org/10.1145/3386252

B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, T. Darrell, “Few-Shot Object Detection via Feature Reweighting”, IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8419-8428, 2019. https://doi.org/10.48550/arXiv.1812.01866

M. Y. Xin, L. W. Ang, S. Palaniappan, “A Data Augmented Method for Plant Disease Leaf Image Recognition based on Enhanced GAN Model Network,” Journal of Informatics and Web Engineering, vol. 2, no. 1, pp 1-12, 2023. https://doi.org/10.33093/jiwe.2023.2.1.1

Z. Shen, Z. Liu, J. Li, Y. G. Jiang, Y. Chen, X. Xue, “DSOD: Learning Deeply Supervised Object Detectors from Scratch”, IEEE International Conference on Computer Vision (ICCV), pp. 1937-1945, 2017. https://doi.org/10.1109/ICCV.2017.212.

K. Saito, Y. Ushiku, T. Harada, K. Saenko, “Strong-Weak Distribution Alignment for Adaptive Object Detection”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6949-6958, 2019. https://doi.org/10.1109/CVPR.2019.00712.

Y. Z. Liu, K. M. Shi, Z. X. Li, G. F. Ding, Y. S. Zou, “Transfer Learning Method for Bearing Fault Diagnosis Based on Fully Convolutional Conditional Wasserstein Adversarial Networks”, Measurement, vol. 180, pp. 109553, 2021. https://doi.org/10.1016/j.measurement.2021.109553

Z. Chen, Y. Fu, K. Chen, Y. G. Jiang, “Image Block Augmentation for One-Shot Learning,” Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 3379–3386, 2019. https://doi.org/10.1609/aaai.v33i01.33013379

Q. Lyu, D. Xia, Y. Liu, X. Yang, R. Li, “Pyramidal Convolution Attention Generative Adversarial Network with Data Augmentation for Image Denoising”, Soft Computing, vol. 25, pp. 9273–9284, 2021. https://doi.org/10.1007/s00500-021-05870-7

S. Lavania and P. S. Matey, “Novel Method for Weed Classification in Maize Field Using OTSU and PCA Implementation”, IEEE International Conference on Computational Intelligence & Communication Technology, pp. 534-537, 2015. https://doi.org/10.1109/CICT.2015.71

H. Altae-Tran, B. Ramsundar, A. S. Pappu, V. Pande, “Low Data Drug Discovery with One-Shot Learning”, ACS Central Science, vol. 3, no. 4, pp. 283–293, 2017. https://doi.org/10.1021/acscentsci.6b00367

G. Daras, A. Odena, H. Zhang, A. G. Dimakis, “Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14519-14527, 2020.

B. Liu, Z. Ding, L. Tian, D. He, S. Li, H. Wang, "Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks", Frontier in Plant Science, vol. 11, 2020. https://doi.org/10.3389/fpls.2020.01082

Y. Xiao, X. Huang, K. Liu, “Model Transferability from ImageNet to Lithography Hotspot Detection,” Journal of Electronic Testing, vol. 37, no.1, pp. 141–149, 2021.

S. Baik, S. Hong, K. M. Lee, “Learning to Forget for Meta-Learning”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2376-2384, 2020.

H. Fukui, T. Hirakawa, T. Yamashita, H. Fujiyoshi, “Attention Branch Network: Learning of Attention Mechanism for Visual Explanation”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10697-10706, 2019. https://doi.org/10.48550/arXiv.1812.10025

J. Hu, L. Shen, G. Sun, “Squeeze-and-Excitation Networks”, IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018. https://doi.org/10.1109/CVPR.2018.00745.

C. Finn, P. Abbeel, S. Levine, “Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”, International Conference on Machine Learning, vol. 70, pp. 1126–1135, 2017. https://doi.org/10.48550/arXiv.1703.03400

W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, J. Luo, “Asymmetric Distribution Measure for Few-Shot Learning”, Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 2957–2963, 2020. https://doi.org/10.48550/arXiv.2002.00153