TY - GEN
T1 - Data Augmentation Using Principal Component Resampling for Image Recognition by Deep Learning
AU - Abayomi-Alli, Olusola Oluwakemi
AU - Damaševičius, Robertas
AU - Wieczorek, Michał
AU - Woźniak, Marcin
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Data augmentation
KW - Deep learning
KW - Image recognition
KW - Principal component analysis
KW - Small data
UR - https://www.scopus.com/pages/publications/85096524265
U2 - 10.1007/978-3-030-61534-5_4
DO - 10.1007/978-3-030-61534-5_4
M3 - Conference contribution
AN - SCOPUS:85096524265
SN - 9783030615338
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 48
BT - Artificial Intelligence and Soft Computing - 19th International Conference, ICAISC 2020, Proceedings
A2 - Rutkowski, Leszek
A2 - Scherer, Rafal
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020
Y2 - 12 October 2020 through 14 October 2020
ER -