Skip to main content

An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification

  • Conference paper
  • First Online:
Dynamics of Information Systems (DIS 2023)

Abstract

Convolutional neural networks (CNNs) can achieve remarkable performance in many computer vision tasks (e.g. classification, detection and segmentation of images). However, the lack of labelled data can significantly hinder their generalization capabilities and limit the scope of their applications. Synthetic data augmentation (DA) is commonly used to address this issue, but uniformly applying global transformations can result in suboptimal performance when certain changes are more relevant to specific classes. The success of DA can be improved by adopting class-specific data transformations. However, this leads to an exponential increase in the number of combinations of image transformations. Finding an optimal combination is challenging due to a large number of possible transformations (e.g. some augmentation libraries offering up to sixty default transformations) and the training times of CNNs required to evaluate each combination. Here, we present an evolutionary approach using a genetic algorithm (GA) to search for an optimal combination of class-specific transformations subject to a feasible time constraint. Our study demonstrates a GA finding augmentation strategies that are significantly superior to those chosen randomly. We discuss and highlight the benefits of using class-specific data augmentation, how our evolutionary approach can automate the search for optimal DA strategies, and how it can be improved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection (2010)

    Google Scholar 

  2. Bäck, T., Schwefel, H.P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)

    Article  Google Scholar 

  3. Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from x-ray images. Inform. Med. Unlocked 20, 100391 (2020)

    Article  Google Scholar 

  4. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2) (2020). https://doi.org/10.3390/info11020125, https://www.mdpi.com/2078-2489/11/2/125

  5. Cubuk, E.D., Zoph, B., Mané, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. CoRR abs/1805.09501 (2018). http://arxiv.org/abs/1805.09501

  6. Dana, H., Ballard, C.M.B.: Computer Vision. Prentice-Hall, Hoboken (1982)

    Google Scholar 

  7. Eastwood, M., et al.: Malignant mesothelioma subtyping of tissue images via sampling driven multiple instance prediction. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds.) Artificial Intelligence in Medicine. AIME 2022. LNCS, vol. 13263, pp. 263–272. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09342-5_25

  8. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43631-8_2

  9. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2014)

    Google Scholar 

  10. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Gan-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018). https://doi.org/10.1016/j.neucom.2018.09.013, https://www.sciencedirect.com/science/article/pii/S0925231218310749

  11. Ge, Y., Zhang, R., Wu, L., Wang, X., Tang, X., Luo, P.: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: CVPR (2019)

    Google Scholar 

  12. Goodfellow, I.J., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge, MA, USA (2016). http://www.deeplearningbook.org

  13. Hauberg, S., Freifeld, O., Larsen, A.B.L., III, J.W.F., Hansen, L.K.: Dreaming more data: class-dependent distributions over diffeomorphisms for learned data augmentation. CoRR abs/1510.02795 (2015). http://arxiv.org/abs/1510.02795

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385

  15. Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)

    Article  Google Scholar 

  16. Khan, S., Javed, M.H., Ahmed, E., Shah, S.A.A., Ali, S.U.: Facial recognition using convolutional neural networks and implementation on smart glasses. In: 2019 International Conference on Information Science and Communication Technology (ICISCT), pp. 1–6 (2019). https://doi.org/10.1109/CISCT.2019.8777442

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). https://doi.org/10.48550/ARXIV.1412.6980, https://arxiv.org/abs/1412.6980

  18. Kirkpatrick, S., Gelatt, C.D., Vecchi, J.M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  19. Koidl, K.: Loss functions in classification tasks. School of Computer Science and Statistic Trinity College, Dublin (2013)

    Google Scholar 

  20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  21. Nivin, T.W., Scott, G.J., Hurt, J.A., Chastain, R.L., Davis, C.H.: Exploring the effects of class-specific augmentation and class coalescence on deep neural network performance using a novel road feature dataset. In: 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–7 (2018). https://doi.org/10.1109/AIPR.2018.8707406

  22. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019). http://arxiv.org/abs/1912.01703

  23. Pereira, S., Correia, J., Machado, P.: Evolving data augmentation strategies. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds.) EvoApplications 2022. LNCS, vol. 13224, pp. 337–351. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02462-7_22

    Chapter  Google Scholar 

  24. Rebuffi, S., Gowal, S., Calian, D.A., Stimberg, F., Wiles, O., Mann, T.A.: Data augmentation can improve robustness. CoRR abs/2111.05328 (2021). https://arxiv.org/abs/2111.05328

  25. S, A.K., Pal, A., Mopuri, K.R., Krishna Gorthi, R.: Adv-cut paste: semantic adversarial class specific data augmentation technique for object detection. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 3632–3638 (2022). https://doi.org/10.1109/ICPR56361.2022.9956409

  26. Shorten, C., Khoshgoftaar, T.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)

    Google Scholar 

  27. Sonka, M., Hlavac, V., Boyle, R.: Image processing, analysis, and machine vision. Cengage Learning (2014)

    Google Scholar 

  28. Terauchi, A., Mori, N.: Evolutionary approach for autoaugment using the thermodynamical genetic algorithm. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9851–9858 (2021)

    Google Scholar 

  29. Ying, X.: An overview of overfitting and its solutions. J. Phys. Conf. Ser. 1168(2), 022022 (2019). https://doi.org/10.1088/1742-6596/1168/2/022022, https://dx.doi.org/10.1088/1742-6596/1168/2/022022

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Silviu Tudor Marc .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Marc, S.T., Belavkin, R., Windridge, D., Gao, X. (2024). An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification. In: Moosaei, H., Hladík, M., Pardalos, P.M. (eds) Dynamics of Information Systems. DIS 2023. Lecture Notes in Computer Science, vol 14321. Springer, Cham. https://doi.org/10.1007/978-3-031-50320-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50320-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50319-1

  • Online ISBN: 978-3-031-50320-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics