The automatic identification in images of people, places, objects, and especially object categories is a central and ongoing challenge within computer vision. This project addresses this problem using low-level image features to learn intermediate representations, ones in which objects in images are labeled with an extensive list of highly descriptive visual attributes. This work demonstrates this approach in three domains: faces, plant species, and architecture.