Image processing based on hybrid semi-supervised learning

Viktor Sineglazov; Kyrylo Lesohorskyi; Andrzej Smolarz

doi:10.1117/12.3057474

16 December 2024 Image processing based on hybrid semi-supervised learning

Viktor Sineglazov, Kyrylo Lesohorskyi, Andrzej Smolarz

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Proceedings Volume 13400, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024; 134000P (2024) https://doi.org/10.1117/12.3057474
Event: Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024, 2024, Lublin, Poland

Abstract

This work is devoted to the development of a new image classification method based on the application of a hybrid semi-supervised learning algorithm for convolutional neural networks. The proposed method is based on the active learning approach and uses metrics to identify and label samples that have a high impact on the certainty metric of the entire neighborhood. To evaluate the effectiveness of the proposed method, a synthetic semi-supervised benchmark dataset is used. The optimal choice of the graph algorithm and clustering algorithm is carried out by testing different combinations of algorithms on the synthetic dataset. The proposed method demonstrates an increase in accuracy compared to the base classifier and more efficient utilization of active labeling compared to supervised active learning. As a result, the possibility of identifying and solving the unknown class problem is realized.

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

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Viktor Sineglazov, Kyrylo Lesohorskyi, and Andrzej Smolarz "Image processing based on hybrid semi-supervised learning", Proc. SPIE 13400, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024, 134000P (16 December 2024); https://doi.org/10.1117/12.3057474

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KEYWORDS

Data modeling

Machine learning

Active learning

Statistical modeling

Image processing

Image classification

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