Nonlinear dictionary learning with application to image classification

Abstract

In this paper, we propose a new nonlinear dictionary learning (NDL) method and apply it to image classification. While a variety of dictionary learning algorithms have been proposed in recent years, most of them learn only a linear dictionary for feature learning and encoding, which cannot exploit the nonlinear relationship of image samples for feature extraction. Even though kernel-based dictionary learning methods can address this limitation, they still suffer from the scalability problem. Unlike existing dictionary learning methods, our NDL employs a feed-forward neural network to seek hierarchical feature projection matrices and dictionary simultaneously, so that the nonlinear structure of samples can be well exploited for feature learning and encoding. To better exploit the discriminative information, we extend the NDL into supervised NDL (SNDL) by learning a class-specific dictionary with the labels of training samples. Experimental results on four image datasets show the effectiveness of the proposed methods.