Structure-Constrained Low-Rank and Partial Sparse Representation with Sample Selection for image classification

In this paper, we propose a novel Structure-Constrained Low-Rank and Partial Sparse Representation algorithm for image classification. First, a Structure-Constrained Low-Rank Dictionary Learning (SCLRDL) algorithm is proposed, which imposes both structure and low-rank restriction on the coefficient matrix. Second, under the assumption that the coefficient of test sample is sparse and correlated with the learned representation of training samples, we propose a Low-Rank and Partial Sparse Representation (LRPSR) algorithm which concatenates training samples and test sample to form a data matrix and finds a low-rank and sparse representation of the data matrix over learned dictionary by low-rank matrix recovery technique. Finally, we design a Sample Selection (SS) procedure to accelerate LRPSR. Experimental results on Caltech 101 and Caltech 256 show that our method outperforms most sparse or low rank based image classification algorithm proposed recently. (C) 2016 Elsevier Ltd. All rights reserved.