This paper presents a descriptor extraction method in the context of image classification, based on a multilayer structure of dictionaries. We propose to learn an architecture of discriminative dictionaries for classification in a supervised framework using a patch-level approach. This method combines many layers of sparse coding and pooling in order to reduce the dimension of the problem. The supervised learning of dictionary atoms allows them to be specialized for a classification task. The method has been tested on known datasets of natural images such as MNIST, CIFAR-10 and STL, in various conditions, especially when the size of the training set is limited, and in a transfer learning application. The results are also compared with those obtained with Convolutional Neural Network (CNN) of similar complexity in terms of number of layers and processing pipeline.

本文提出了一种基于字典多层结构的图像分类上下文中的描述符提取方法。我们建议使用补丁程序级别的方法在监督框架中学习区分词典的体系结构。此方法结合了稀疏编码和池化的许多层，以减小问题的范围。字典原子的监督学习使它们可以专门用于分类任务。该方法已经在各种条件下，特别是在训练集的大小受到限制的情况下，以及在转移学习应用程序中，在已知的自然图像数据集（例如MNIST，CIFAR-10和STL）上进行了测试。