The problem addressed in this paper is feature extraction and classification of images. As a solution, we proposed a Deep Wavelet Network architecture based on the Wavelet Network and the Stacked Auto-encoders. In this work, we shifted from the deep learning based on neural networks to deep learning based on wavelet networks. The latter doesn’t change the general form of the Deep Learning based on the Neural Network but it is a novel method that shows the process of feature extraction and explains the system of image classification. Our Deep Wavelet Network is created for the training and the classification phase. After the training phase, a linear classifier is applied. Finally, the experimental test of our method is in the COIL-100 dataset.
Image classification is an area where deep learning and especially stacked Auto-encoders have really proven their strength. The contributions of this paper lie in the creation of a new classifier to remedy some classification problems. This new method of classification presents a combination of the most used techniques in Deep Learning (DL) and Sparse Coding (SC) in the field of classification. Proposed deep neural networks consist of three stacked Auto-encoders and a Softmax used as an outer layer for classification. The first Auto-encoder is created from a sparse representation of all images of the dataset. The sparse representation of all images represents the decoder part of the first Auto-encoder. Then the transpose of the matrix is applied to get the encoder part. Experiments performed on standard datasets such as ImageNet and the Coil-100 reveal the efficacy of this approach.
KEYWORDS: Wavelets, Associative arrays, Fast wavelet transforms, Databases, Chemical species, Image processing, Data modeling, Linear filtering, Algorithm development, Denoising
Classical signal representation techniques generally use a description of the components on a basis on which the representation of the signal is unique such as wavelets network. Conversely, sparse representations consist in the decomposition of the signal on a dictionary comprising a number of elements much larger than the dimension of the signal. This technique can be widely used for representation, compression, denoising and separation of all types of signals. Consequently, some researches have confirmed that the use of a predefined dictionary is less efficient than a dictionary from training data. So, the idea of this paper is to propose a new technique for the creation of a dictionary using the wavelet decomposition to enhance the sparse representation of images. This technique is based on the combination of sparse coding and the fast wavelet transform algorithms for image representation. Our results obtained using different universal image databases showed greater performances in the representation of images when compared to some methods from the state of the art.
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