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Data Augmentation Using Principal Component Resampling for Image Recognition by Deep Learning

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12416))

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Abstract

Image recognition by deep learning usually requires many sample images to train. In case of a small number of images available for training, data augmentation techniques should be applied. Here we propose a novel image augmentation technique based on a random permutation of coefficients of within-class principal components obtained after applying Principal Component Analysis (PCA). After reconstruction, newly generated surrogate images are employed to train a deep network. In this study, we demonstrated the applicability of our approach on training a custom convolutional neural network using the CIFAR-10 image dataset. The experimental results show an improvement in terms of classification accuracy and classification ambiguity.

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Acknowledgments

Authors acknowledge contribution to this project of the Program “Best of the Best 4.0” from the Polish Ministry of Science and Higher Education No. MNiSW/2020/43/DIR/NN4.

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Authors and Affiliations

  1. Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Olusola Oluwakemi Abayomi-Alli & Robertas Damaševičius

  2. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

    Robertas Damaševičius, Michał Wieczorek & Marcin Woźniak

Authors
  1. Olusola Oluwakemi Abayomi-Alli
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  2. Robertas Damaševičius
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  3. Michał Wieczorek
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  4. Marcin Woźniak
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Corresponding author

Correspondence to Marcin Woźniak .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Abayomi-Alli, O.O., Damaševičius, R., Wieczorek, M., Woźniak, M. (2020). Data Augmentation Using Principal Component Resampling for Image Recognition by Deep Learning. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-61534-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61533-8

  • Online ISBN: 978-3-030-61534-5

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