- Scene recognition systems which attempt to deal with a large number of scene categories currently lack proper knowledge about the perceptual ontology of scene categories and would enjoy significant advantage from a perceptually meaningful scene representation. In this work we perform a large-scale human study to create “SceneNet”, an online ontology database for scene understanding that organizes scene categories according to their perceptual relationships. This perceptual ontology suggests that perceptual relationships do not always conform the semantic structure between categories, and it entails a lower dimensional perceptual space with “perceptually meaningful” Euclidean distance, where each embedded category is represented by a single prototype. Using the SceneNet ontology and database we derive a computational scheme for learning non-linear mapping of scene images into the perceptual space, where each scene image is closest to its category prototype than to any other prototype by a large margin. Then, we demonstrate how this approach facilitates improvements in large-scale scene categorization over state-of-the-art methods and existing semantic ontologies, and how it reveals novel perceptual findings about the discriminative power of visual attributes and the typicality of scenes.