A Multi-Scale Feature Attention Image Recognition Algorithm

Authors

• Xin Ming Yuan, Malaysia University of Science and Technology, Malaysia
• Ang Ling Weay, Malaysia University of Science and Technology, Malaysia
• Sellappan Palaniappan, Malaysia University of Science and Technology, Malaysia

Corresponding Author ORCID Identifer

Xin MingYuan https://orcid.org/0000-0001-9336-5612

Section

Regular Issue

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.

Cite This Article

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

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