This paper proposes a general method for size optimization in dense sampling to obtain a better representation of an image. Our method can be utilized to improve the performance of image classification and other tasks. We discuss the spatial consistency in global-scope restrained descriptors, by analyzing the appropriate sampling size. We apply the low rank method to solve the representative matrix of the descriptor sets at different scales, and obtain the optimized dense sampling size according to the lowest ranks of the representative matrices. Experimental results indicate that the proposed method gives an innovative and effective image representation, and it outperforms traditional dense sampling without size optimization.