Hierarchical methods have been widely explored for object recognition, which is a critical component of scene understanding. However, few existing works are able to model the contextual information (e.g., objects co-occurrence) explicitly within a single coherent framework for scene understanding. Towards this goal, in this paper we propose a novel three-level (superpixel level, object level and scene level) hierarchical model to address the scene categorization problem. Our proposed model is a coherent probabilistic graphical model that captures the object co-occurrence information for scene understanding with a probabilistic chain structure. The efficacy of the proposed model is demonstrated by conducting experiments on the LabelMe dataset.

2012 23rd British Machine Vision Conference, BMVC 2012
School of Computer Science

Li, X. (Xin), & Guo, Y. (2012). An object co-occurrence assisted hierarchical model for scene understanding. In BMVC 2012 - Electronic Proceedings of the British Machine Vision Conference 2012. doi:10.5244/C.26.81