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A dyadic multi-resolution deep convolutional neural wavelet network for image classification

Authors:

Mourad ZaiedAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 5

Pages 6149 - 6163

https://doi.org/10.1007/s11042-017-4523-2

Published: 01 March 2018 Publication History

Abstract

For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets' network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article.

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Cited By

Kissel MDiepold K(2023)Structured Matrices and Their Application in Neural Networks: A SurveyNew Generation Computing10.1007/s00354-023-00226-141:3(697-722)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s00354-023-00226-1
Smiti SSoui M(2020)Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTEInformation Systems Frontiers10.1007/s10796-020-10031-622:5(1067-1083)Online publication date: 3-Aug-2020
https://dl.acm.org/doi/10.1007/s10796-020-10031-6
Rashid MKhan MSharif MRaza MSarfraz MAfza F(2019)Object detection and classificationMultimedia Tools and Applications10.1007/s11042-018-7031-078:12(15751-15777)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-7031-0
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A dyadic multi-resolution deep convolutional neural wavelet network for image classification
1. Computing methodologies
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      1. Supervised learning
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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 5

March 2018

1287 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2018

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Cited By

Kissel MDiepold K(2023)Structured Matrices and Their Application in Neural Networks: A SurveyNew Generation Computing10.1007/s00354-023-00226-141:3(697-722)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s00354-023-00226-1
Smiti SSoui M(2020)Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTEInformation Systems Frontiers10.1007/s10796-020-10031-622:5(1067-1083)Online publication date: 3-Aug-2020
https://dl.acm.org/doi/10.1007/s10796-020-10031-6
Rashid MKhan MSharif MRaza MSarfraz MAfza F(2019)Object detection and classificationMultimedia Tools and Applications10.1007/s11042-018-7031-078:12(15751-15777)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-7031-0
Gnouma MLadjailia AEjbali RZaied M(2019)Stacked sparse autoencoder and history of binary motion image for human activity recognitionMultimedia Tools and Applications10.1007/s11042-018-6273-178:2(2157-2179)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11042-018-6273-1
Zhao LWang ZZhang GQi YWang X(2018)Eye state recognition based on deep integrated neural network and transfer learningMultimedia Tools and Applications10.1007/s11042-017-5380-877:15(19415-19438)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11042-017-5380-8

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