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International Symposium on Neural Networks

ISNN 2018: Advances in Neural Networks – ISNN 2018 pp 300–309Cite as

Fourier Transform-Based Image Classification Using Complex-Valued Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10878))

Abstract

Although complex-valued convolutional neural networks (CVCNNs) have shown an improvement over their real-valued counterparts (RVCNNs) when trained on real-valued images, in order to harness the full potential of CVCNNs, they should be used with fully complex-valued inputs. Because every image has one and only one Fourier transform, the problem of classifying real-valued images is equivalent to the problem of classifying their complex-valued Fourier transforms. Experiments done using the MNIST, SVHN, and CIFAR-10 datasets show an improved performance of CVCNNs trained on Fourier-transformed images over CVCNNs trained on real-valued images (for which the imaginary part of the input is considered to be zero), and over RVCNNs.

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Acknowledgement

This work was supported by research grant no. PCD-TC-2017-41 of the Polytechnic University Timişoara, Romania.

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Authors and Affiliations

  1. Department of Computer and Software Engineering, Polytechnic University Timişoara, Blvd. V. Pârvan, No. 2, 300223, Timişoara, Romania

    Călin-Adrian Popa & Cosmin Cernăzanu-Glăvan

Authors
  1. Călin-Adrian Popa
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  2. Cosmin Cernăzanu-Glăvan
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Corresponding author

Correspondence to Călin-Adrian Popa .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

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Popa, CA., Cernăzanu-Glăvan, C. (2018). Fourier Transform-Based Image Classification Using Complex-Valued Convolutional Neural Networks. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-92537-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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