Supervised Image Classification Using Deep Convolutional Wavelets Network

This paper gives a review of the deep learning history and proposes a new approach to supervised image classification 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. The experimental test of our approach performed on "COIL-100" dataset demonstrates that our model is remarkably efficient for image classification compared to a known classifier.