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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Aqeel M. Hamad Alhussainy
Al-Nahrain University
Computer Science & IT

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Half Gaussian-based wavelet transform for pooling layer for convolution neural network Aqeel M. Hamad Alhussainy; Ammar D. Jasim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16398

Abstract

Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling.
A novel pooling layer based on gaussian function with wavelet transform Aqeel M. Hamad alhussainy; Ammar D. Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1289-1298

Abstract

Convolution represent basic layer in the convolutional neural network, but it can result in big size of the data, which may increase the complexity of the network. Different pooling methods are used to perform down sample these data. In this paper, we have proposed a novel pooling method by using Gaussian function to determine the wavelet filter coefficients. At first, the basic statistics are determined for each pool size of the signal, then Gaussian probability distribution function is determined. According to the procedure of extracting the features, three methods are proposed, the first method is used the normalized values of basic statistics as wavelet filter to be multiplied by original signal, the second method used the determined statistics as features of the original signal, then multiplied it with constant wavelet filter based on Gaussian, while the third method is similar to first method, except it depend on entire signal instead of each pool size. The proposed methods are combined with other standard methods such as max and pooling. The experiments are performed on different datasets and the results show that the proposed methods perform or outperform other methods and can increase performance of the (CNN).
Co-Authors Ammar D. Jasim