Semi-supervised image classification algorithm based on double threshold FixMatch

Rui Li; Kun Liu

doi:10.1117/12.3031629

18 July 2024 Semi-supervised image classification algorithm based on double threshold FixMatch

Rui Li, Kun Liu

Proceedings Volume 13179, International Conference on Optics and Machine Vision (ICOMV 2024); 131791J (2024) https://doi.org/10.1117/12.3031629
Event: International Conference on Optics and Machine Vision (ICOMV 2024), 2024, Nanchang, China

Abstract

To address the problem of how to improve the utilisation of a large number of unlabelled samples and the accuracy of classification using a few labelled ship image samples, this paper proposes a semi-supervised ship image classification network based on Double threshold FixMatch (DT-FixMatch). Firstly, double threshold is introduced into the FixMatch, and the model predicts the unlabelled images after weak enhancement, and retains the category of the results with a certain confidence level higher than the high threshold and transforms them into pseudo-labels; for the results higher than the low threshold, the softmax output value of the prediction result is directly used to compare it with the model's softmax output value of the strongly-enhanced version of the same image, and calculates the Mean Square Error (MSE) loss, which allows the low confidence samples to be fully utilised. Secondly, to reduce the influence of noisy pseudo-labels, we have implemented label smoothing and consistency regularization. Additionally, we have incorporated an improved attention mechanism into the backbone network, concatenated with I_CBAM, which enhances ResNeXt's ability to extract potentially critical features from fuzzy ship images. The experimental results, based on DataCastle's public dataset, indicate that the model achieves a classification accuracy of 92.86%, precision of 87.68%, and F1-Score of 81.67% when using only 5 images with 50 labelled images in each of the ten different ship images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Li and Kun Liu "Semi-supervised image classification algorithm based on double threshold FixMatch", Proc. SPIE 13179, International Conference on Optics and Machine Vision (ICOMV 2024), 131791J (18 July 2024); https://doi.org/10.1117/12.3031629

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KEYWORDS

Statistical modeling

Data modeling

Image classification

Image enhancement

Performance modeling

Machine learning

Feature extraction

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