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In this paper, we propose a three-layer spatial sparse coding (TSSC) for image classification, aiming at three objectives: naturally recognizing image categories without learning phase, naturally involving spatial configurations of images, and naturally counteracting the intra-class variances. The method begins by representing the test images in a spatial pyramid as the to-be-recovered signals, and taking all sampled image patches at multiple scales from the labeled images as the bases. Then, three sets of coefficients are involved into the cardinal sparse coding to get the TSSC, one to penalize spatial inconsistencies of the pyramid cells and the corresponding selected bases, one to guarantee the sparsity of selected images, and the other to guarantee the sparsity of selected categories. Finally, the test images are classified according to a simple image-to-category similarity defined on the coding coefficients. In experiments, we test our method on two publicly available datasets and achieve significantly more accurate results than the conventional sparse coding with only a modest increase in computational complexity.