Robust scene classification by Gist with angular radial partitioning

摘要

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.