Exploiting photographic style for category-level image classification by generalizing the spatial pyramid

This paper investigates the use of photographic style for category-level image classification. Specifically, we exploit the assumption that images within a category share a similar style defined by attributes such as colorfulness, lighting, depth of field, 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 classification 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.