Noise learning based discriminative dictionary learning algorithm for image classification

Abstract

Dictionary learning is an efficient and effective method to preserve the label property in supervised learning for image classification. However, different noises in the image samples may cause unstable residuals during the training stage, which is particularly reflected in obtaining an inaccurate dictionary and inefficient utilization rate of label information. In order to fully exploit the supervised information for learning a discriminative dictionary, we propose an effective dictionary learning algorithm for designing structured dictionary where each atom related to a corresponding label. The proposed algorithm is implemented by alternating direction method of multipliers (ADMM) based on noise learning, where the noise is composed of interference signals and reconstruction residuals. In the training stage, we first adopt cross-label suppression method to enlarge the difference among the representations of different labels. Meanwhile, a mathematical operator of Laplacian matrix in spectral clustering named N-cut is also utilized to shorten the difference among the representations of same labels. In the testing stage, to take fully advantage of the learnt dictionary, two efficient classifiers of global coding and local coding are adopted in the denoising step respectively. Experiments are conducted in different datasets including face recognition, scene classification, object categorization, and dynamic texture categorization. Simulation results confirm our proposed method in terms of both classification performance and computational efficiency.