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An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding
This video was recorded at British Machine Vision Conference (BMVC), Surrey 2012. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
