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Finding corresponding image points is a challenging computer vision problem, especially for confusing scenes with surfaces of low textures or repeated patterns. Despite the well-known challenges of extracting conceptually meaningful high-level matching primitives, many recent works describe high-level image features such as edge groups, lines and regions, which are more distinctive than traditional local appearance based features, to tackle such difficult scenes. In this paper, we propose a different and more general approach, which treats the image matching problem as a recognition problem of spatially related image patch sets. We construct augmented semi-global descriptors (ordinal codes) based on subsets of scale and orientation invariant local keypoint descriptors. Tied ranking problem of ordinal codes is handled by increasingly keypoint sampling around image patch sets. Finally, similarities of augmented features are measured using Spearman correlation coefficient. Our proposed method is compatible with a large range of existing local image descriptors. Experimental results based on standard benchmark datasets and SURF descriptors have demonstrated its distinctiveness and effectiveness.