Abstract The random permutations and combinations of local images in image recognition tasks are complex problems. In this paper,an algorithm based on kernel sparse representation classification and multi-scale block rotation-extension (KSRC-MSBRE) is proposed to solve these problems. Firstly,the multi-scale grids are used to segment the training image,and the rotation-extended methods are applied to create a dictionary which adapts to the random permutations and the combinations of local images in test sets. To enhance the sparsity of the dictionary and improve the efficiency of the system,a new strategy is proposed to reduce the dimensions of the dictionary. Then,a kernel random coordinate descent method is proposed to solve the convex optimization problem in the KSRC. The experimental results show the proposed method has robust performance when dealing with the random permutations and the combinations of local images,and it outperforms other classical image recognition methods.
2013, Vol. 26 
