Robust scene classification by Gist with angular radial partitioning

Natural scene recognition and classification have received considerable attention in the computer vision community due to its challenging nature. Significant intra-class variations have largely limited the accuracy of scene categorization tasks: a holistic representation forces matching in strict spatial confinement; whereas a bag of features representation ignores the order or spatial layout of the scene completely, resulting in a loss of scene logic. In this paper, we present a novel method, called ARP (Angular Radial Partitioning) Gist, to classify the scene. Experiments show that the proposed method has improved recognition accuracy by better representing the structure in a scene and striking a balance between spatial confinement and freedom.