A novel approach involving the comparison of appearance and geometrical similarity of local patterns via a combined description is presented. Candidate groups of interest points are identified based on unlikeliness of being matched by chance. For each of the keypoints in these groups, a novel description is proposed. This description utilizes quantized appearance descriptors of interest points to avoid the necessity of matching each test descriptor to each template descriptor. Additionally, one-to-many matching is possible in contrast to its counterparts in the literature. Geometrical descriptions are based on multiple small groups of points, namely quads, in barycentric coordinates, instead of a single large group that is susceptible to partial transformations. These advantages render the proposed algorithm robust to significant appearance changes, especially due to affine transformations, while being resistant to random false matches through simultaneous utilization of geometrical part of the descriptor. This generic, robust template matching technique is evaluated in an application of scene logo retrieval.