Context-Based Scene Understanding

Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context semantic, spatial, scale and scope of the relations pairwise, high-order. In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two distributions. First model incorporates context descriptor along with annotation response into a supervised Latent Dirichlet Allocation LDA built on multi-variate Bernoulli distribution called Context-Based LDA CBLDA. The second model is based on multi-variate Wallenius' non-central Hyper-geometric distribution and is called Wallenius LDA WLDA. WLDA incorporates context knowledge as bias parameter. Scene context is modeled as a graph and effectively used in object detection framework to maximize semantical consistency of the scene. The graph can also be used in recognition of out-of-context objects. Annotation metadata of Sun397 dataset is used to construct the context model. Performance of the proposed approaches was evaluated on ImageNet dataset. Comparison between proposed approaches and state-of-art multi-class object annotation algorithm shows superiority of presented approach in labeling of scene content.