An architecture of Distributed Beta Wavelet Networks for large image classification in MapReduce

Sakkari, Mohamed; Zaied, Mourad

doi:10.1109/isda.2015.7489171

Cited by 4 publications

(2 citation statements)

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“…Wavelet transform (WT) is often used in deep learning [5,16,24]. Many features can be obtained by the discrete wavelet transform which have been improved by researches.…”

Section: Introductionmentioning

confidence: 99%

Research on improved wavelet convolutional wavelet neural networks

et al. 2020

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Convolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

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“…Wavelet transform (WT) is often used in deep learning [5,16,24]. Many features can be obtained by the discrete wavelet transform which have been improved by researches.…”

Section: Introductionmentioning

confidence: 99%

Research on improved wavelet convolutional wavelet neural networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Rotation and illumination changes are handled well by ordering pixels according to their intensities and gradient orientations. Sakkari and Zaied [34] presented a new architecture of distributed beta wavelet networks for large image classification in the MapReduce model. To prove the performance of wavelet networks, a parallelised learning algorithm based on the beta wavelet transform is proposed and realised in the MapReduce model.…”

Section: Resultsmentioning

confidence: 99%