A New Semantic Approach for CBIR Based on Beta Wavelet Network Modeling Shape Refined by Texture and Color Features

ElAdel, Asma; Ejbali, Ridha; Zaied, Mourad; Amar, Chokri Ben

doi:10.1007/978-3-319-10840-7_46

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Yun LeCun invented the sparce representations for image classification and object recognition [8], [9]. On the other hand, the wavelet networks (WN) [10], [12] are a special case of neural networks. Wavelet neural networks (WNN) are the combination of two theories: the wavelets and the neural networks.…”

Section: Introductionmentioning

confidence: 99%

A deep convolutional neural wavelet network to supervised Arabic letter image classification

Hassairi

Ejbali

Zaied

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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In this paper, a new approach to supervised image classification is suggested. It's conducted by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a convolutional deep neural wavelet network. This network is obtained using a series of stacked auto-encoders and a linear classifier. Finally, a local contrast normalization and an intelligent pooling are applied to our network.The experimental test of our approach performed on Arabic Printed Text Image (APTI) dataset demonstrates that our model is remarkably efficient for image classification compared to a known classifier.

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Section: Introductionmentioning

confidence: 99%

A deep convolutional neural wavelet network to supervised Arabic letter image classification

Hassairi

Ejbali

Zaied

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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“…Sparse representations for object recognition and image classification was developed by Yann LeCun (Jarrett et al, 2009;LeCun, 2012;Liu et al, 2016). The work of Daugman (2003) is the origin of Wavelet Networks (WN) (Amar et al, 2005;ElAdel et al, 2014;Ejbali et al, 2010;Zaied et al, 2012), in which Gabor wavelets have been used for image classification. The WN have become popular after the work of Pati and Krishnaprasad (1993), Zhang and Benveniste (1992) and Szu et al (1992;Iyengar et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

Ali¹,

Ejbali²,

Zaied³

2018

Journal of Computer Science

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This work present a new solution for medical image classification using the Neural Network (NN) and Wavelet Network (WN) based on the Fast Wavelet Transform (FWT) and the Adaboost algorithm. This method is divided in two stages: The learning stage and the classification stage. The first consists to extract the features using the FWT based on the MultiResolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Then, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. The second consist to create an AutoEncoder (AE) using the bestselected wavelets of all images. Then, after a series of Stacked AE, a pooling is applied for each hidden layer to get our Convolutional Deep Neural Wavelet Network (CDNWN) architecture for the classification phase. Our experiments were performed on two different datasets and the obtained classifications rates given by our approach show a clear improvement compared to those cited in this article.

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A deep stacked wavelet auto-encoders to supervised feature extraction to pattern classification

Hassairi

Ejbali

Zaied

2017

Multimed Tools Appl

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A New Semantic Approach for CBIR Based on Beta Wavelet Network Modeling Shape Refined by Texture and Color Features

Cited by 8 publications

References 14 publications

A deep convolutional neural wavelet network to supervised Arabic letter image classification

A deep convolutional neural wavelet network to supervised Arabic letter image classification

A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

A deep stacked wavelet auto-encoders to supervised feature extraction to pattern classification

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