Context-Based Scene Understanding

Esfandiar Zolghadr and Borko Furht
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Esfandiar Zolghadr: Florida Atlantic University, Boca Raton, FL, USA
Borko Furht: Department of Computer and Electrical, Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2016, vol. 7, issue 1, 22-40

Abstract: 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.

Date: 2016
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