Optimizing CNN using Fast Fourier Transformation for Object Recognition

Varsha Nair, Moitrayee Chatterjee, Neda Tavakoli, Akbar Siami Namin, Craig Snoeyink

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes to use Fast Fourier Transformation-based U-Net (a refined fully convolutional networks) and perform image convolution in neural networks. Leveraging the Fast Fourier Transformation, it reduces the image convolution costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the overall computational costs. The proposed model identifies the object information from the images. We apply the Fast Fourier transform algorithm on an image data set to obtain more accessible information about the image data, before segmenting them through the U-Net architecture. More specifically, we implement the FFT-based convolutional neural network to improve the training time of the network. The proposed approach was applied to publicly available Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated improvement in training time during convolution from 600 - 700 ms/step to 400-500 ms/step. We evaluated the accuracy of our model using Intersection over Union (IoU) metric showing significant improvements.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-239
Number of pages6
ISBN (Electronic)9781728184708
DOIs
StatePublished - Dec 2020
Event19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States
Duration: Dec 14 2020Dec 17 2020

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

Conference19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
CountryUnited States
CityVirtual, Miami
Period12/14/2012/17/20

Keywords

  • CNN
  • Fast Fourier Transformation
  • Image Processing
  • Object Recognition

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