Arrangement based image representation for scene recognition

Studies on human faculties of scene recognition have lead to two broad classifications of the perceived information: local and global. It has been shown that both are processed separately and combined towards final category assignment. Recently, it was suggested that accuracy of computational models for local information closely match human performance, while it is not so for current global representations. In this paper, we propose a new global representation, AGIR. The key differences we propose to current approaches is the explicit modeling of `arrangement' (co-occurrence and configuration) in the scene, and use of multiple hierarchical dictionaries. The effectiveness of the proposed scheme is shown through various experiments and comparisons on both indoor and outdoor scene recognition.