A Cortical Framework for Scene Categorisation

Human observers can very rapidly and accurately categorise scenes. This is context or gist vision. In this paper we present a biologically plausible scheme for gist vision which can be integrated into a complete cortical vision architecture. The model is strictly bottom-up, employing state-of-the-art models for feature extractions. It combines five cortical feature sets: multiscale lines and edges and their dominant orientations, the density of multiscale keypoints, the number of consistent multiscale regions, dominant colours in the double-opponent colour channels, and significant saliency in covert attention regions. These feature sets are processed in a hierarchical set of layers with grouping cells, which serve to characterise five image regions: left, right, top, bottom and centre. Final scene classification is obtained by a trained decision tree.