Convolutional wavelet blocks in image classification

  • Uladzislau A. Varabei Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus
  • Alexander E. Malevich Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

Abstract

In this paper, based on an image classification problem and wavelet family CDF-9/7, it is shown how to incorporate discrete wavelet transform into a computer vision model, while maintaining the ability of its training with the backpropagation method. A convolutional wavelet block, that extracts features at different levels of decomposition of the incoming signal, is proposed and successfully integrated into a set of neural network models. The blocks implemented allow to reduce the original model size by 30 – 40 %, while maintaining comparable quality in terms of metric. An effective method for evaluation of discrete wavelet transform on graphics processing unit with lifting scheme is presented. The implementation of wavelet blocks uses element-wise operations of additions and multiplications, thus allowing a simple export of a trained model into one of desired formats for running on new data. ResNetV2-50, MobileNetV2 and EfficientNetV2-B0 architectures are used as the basis models. A new dataset, which is based on a set of categories of LSUN dataset, is constructed for conducting experiments.

Author Biographies

Uladzislau A. Varabei, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

postgraduate student at the department of differential equations and system analysis, faculty of mechanics and mathematics

Alexander E. Malevich, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

PhD (physics and mathematics), docent; associate professor at the department of differential equations and system analysis, faculty of mechanics and mathematics

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Published
2024-07-19
Keywords: neural networks, deep learning, wavelets, discrete wavelet transform, image classification
How to Cite
Varabei, U. A., & Malevich, A. E. (2024). Convolutional wavelet blocks in image classification. Journal of the Belarusian State University. Mathematics and Informatics, 2, 93-103. Retrieved from https://journals.bsu.by/index.php/mathematics/article/view/6084
Issue
No 2 (2024): Journal of the Belarusian State University. Mathematics and Informatics
Section
Theoretical Foundations of Computer Science

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