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.