Fourier-Based Image Classification Using CNN
Abstract
Recently, Convolutional Neural Networks (CNNs) have achieved remarkable success in
computer vision, image processing and image processing tasks. Traditional CNN models
work directly with spatial domain images. On the other hand, images obtained with Fast
Fourier Transform (FFT) represent the Frequency domain and provide an advantage in
computational cost by reducing potential calculation complexity. This study uses FFT
converted images as input to the CNN algorithm to increase image classification and
recognition accuracy and investigates the effects of this. The study begins with a
comprehensive review of the foundations and features of FFT. It assumes that by
converting the input images from the Spatial domain to the Frequency domain, the input
image can be learned more efficiently and better results can be achieved in terms of
performance by studying the most important features in the Frequency domain. To evaluate
the effectiveness of this assumption, CIFAR-10, MNIST-Digits and MNIST-Fashion
datasets were used. As a result, it has been shown that FFT-based preprocessing can
improve classification accuracy, especially in cases where the datasets contain
high-frequency noise, and it has shown different results in different datasets. Therefore, it
is thought that the effect of FFT preprocessing varies depending on the datasets.
Keywords
Machine Learning , Image Classification , Frequency Domain , Deep Learning
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