Sparse representation algorithm for image recognition based on the combination of structured sparse and atom sparse

Two modified structured sparse representation algorithm for image recognition is proposed aiming at the two problems that the selection of the sparse criterion and the blocks’ division in the dictionary. First of all, according to there are more coefficients that is not zero in the structured sparse criterion, the thought of combining the structured sparse criterion and the atom sparse criterion is proposed, including both parallel and serial manner. In parallel combination, weighted summation of the both is used as the discriminant criterion. In serial combination, the dictionary is reconstructed after structured sparse representation, and then the atom sparse representation is used to achieve classification. Then, according to the problem that the blocks’ division of samples from the same class in the dictionary, structured sparse representation algorithm for recognition based on MLP is proposed. The images in the same class are divided into blocks based on MLP first to ensure that each block lies in low dimension linear subspaces respectively. After that, the test image is recognized by structured sparse representation. Experimental results show that both of the algorithms are effective.