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Comparative analysis of data augmentation methods for image modality
| Назва | Comparative analysis of data augmentation methods for image modality |
| Назва англійською | Comparative analysis of data augmentation methods for image modality |
| Автори | Andrii Bokhonko, Nataliia Melnykova, Yurii Patereha |
| Принадлежність | Lviv Polytechnic National University, Lviv, Ukraine |
| Бібліографічний опис | Comparative analysis of data augmentation methods for image modality / Andrii Bokhonko, Nataliia Melnykova, Yurii Patereha // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 113. — No 1. — P. 16–26. |
| Bibliographic description: | Bokhonko A., Melnykova N., Patereha Y. (2024) Comparative analysis of data augmentation methods for image modality. Scientific Journal of TNTU (Tern.), vol 113, no 1, pp. 16–26. |
| УДК |
004 |
| Ключові слова |
data modality, classification, data augmentation, sample expansion, machine learning |
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The object of research is forecasting processes in the case of short sets of tabular data. The subject of research is the data augmentation method for images. Achieving the goal occurs primarily from the study of existing machine learning tools and data augmentation methods for images. Further software development to implement various data augmentation methods and machine learning models for images. Approbation of the work was carried out by analyzing the effectiveness of various methods of data augmentation for images using quality metrics and statistical methods. Due to the results of the research, an analysis of the influence of various methods of data augmentation on the effectiveness of classifiers in images was carried out. |
| ISSN: | 2522-4433 |
| Перелік літератури |
1. A. Soofi і A. Awan Classification Techniques in Machine Learning: Applications and Issues. Journal of Basic & Applied Sciences. Vol. 13. 2017. Р. 459–465.
2. A. L. C. Ottoni, R. M. de Amorim, M. S. Novo, і D. B. Costa Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets. Intl. J. Mach. Learn. Cybern. Vol. 14. 2023. Р. 171–186.
3. L. Taylor і G. Nitschke Improving deep learning with generic data augmentation, in 2018 IEEE symposium series on computational intelligence (SSCI). 2018. Р. 1542–1547.
4. A. Mikołajczyk і M. Grochowski Data augmentation for improving deep learning in image classification problem, in 2018 international interdisciplinary PhD workshop (IIPhDW). 2018. Р. 117–122.
5. D. Lewy і J. Mańdziuk An overview of mixing augmentation methods and augmentation strategies. Artif Intell Rev. 56 (3). 2023. Р. 2111–2169.
6. C. Shorten і T. M. Khoshgoftaar A survey on Image Data Augmentation for Deep Learning. J. Big Data. 6 (1). 2019.
7. K. Dunphy, M. N. Fekri, K. Grolinger, і A. Sadhu Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information. Sensors. 22. (16), 2022.
8. M. Hossin і S. M.N A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process. Vol. 5. 2015. Р. 1–11. |
| References: |
1. A. Soofi і A. Awan Classification Techniques in Machine Learning: Applications and Issues. Journal of Basic & Applied Sciences. Vol. 13. 2017. Р. 459–465.
2. A. L. C. Ottoni, R. M. de Amorim, M. S. Novo, і D. B. Costa Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets. Intl. J. Mach. Learn. Cybern. Vol. 14. 2023. Р. 171–186.
3. L. Taylor і G. Nitschke Improving deep learning with generic data augmentation, in 2018 IEEE symposium series on computational intelligence (SSCI). 2018. Р. 1542–1547.
4. A. Mikołajczyk і M. Grochowski Data augmentation for improving deep learning in image classification problem, in 2018 international interdisciplinary PhD workshop (IIPhDW). 2018. Р. 117–122.
5. D. Lewy і J. Mańdziuk An overview of mixing augmentation methods and augmentation strategies. Artif Intell Rev. 56 (3). 2023. Р. 2111–2169.
6. C. Shorten і T. M. Khoshgoftaar A survey on Image Data Augmentation for Deep Learning. J. Big Data. 6 (1). 2019.
7. K. Dunphy, M. N. Fekri, K. Grolinger, і A. Sadhu Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information. Sensors. 22. (16), 2022.
8. M. Hossin і S. M.N A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process. Vol. 5. 2015. Р. 1–11. |
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