Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparison of Different Image Data Augmentation Approaches

Version 1 : Received: 27 October 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (11:18:23 CET)

A peer-reviewed article of this Preprint also exists.

Nanni, L.; Paci, M.; Brahnam, S.; Lumini, A. Comparison of Different Image Data Augmentation Approaches. J. Imaging 2021, 7, 254. Nanni, L.; Paci, M.; Brahnam, S.; Lumini, A. Comparison of Different Image Data Augmentation Approaches. J. Imaging 2021, 7, 254.

Journal reference: J. Imaging 2021, 7, 254
DOI: 10.3390/jimaging7120254

Abstract

Convolutional Neural Networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the Discrete Wavelet Transform and the other on the Constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across three benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art performance across all three data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification (code available at https://github.com/LorisNanni).

Keywords

Data augmentation; Deep Learning; Convolutional Neural Networks; Ensemble.

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.