Abstract

Several state-of-the-art Generic Visual Categorization (GVC) systems are built around a vocabulary of visual terms and char- acterize images with one histogram of visual word counts. We propose a novel and practical approach to GVC based on a universal vocabu- lary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the univer- sal vocabulary using class-specific data. An image is characterized by a set of histograms - one per class - where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. It is shown experimentally on three very different databases that this novel representation outperforms those approaches which characterize an image with a single histogram.