This paper investigates the use of photographic style for category-level image classi cation. Speci cally, we exploit the assumption that images within a category share a similar style de ned by attributes such as colorfulness, lighting, depth of eld, viewpoint and saliency. For these style attributes we create correspondences across images by a generalized spatial pyramid matching scheme. Where the spatial pyramid groups features spatially, we allow more general feature grouping and in this paper we focus on grouping images on photographic style. We evaluate our approach in an object classi cation task and investigate style differences between professional and amateur photographs. We show that a generalized pyramid with style-based attributes improves performance on the professional Corel and amateur Pascal VOC 2009 image datasets.