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Context-Based Scene Understanding

Context-Based Scene Understanding

Esfandiar Zolghadr, Borko Furht
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 19
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466690448|DOI: 10.4018/IJMDEM.2016010102
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MLA

Zolghadr, Esfandiar, and Borko Furht. "Context-Based Scene Understanding." IJMDEM vol.7, no.1 2016: pp.22-40. http://doi.org/10.4018/IJMDEM.2016010102

APA

Zolghadr, E. & Furht, B. (2016). Context-Based Scene Understanding. International Journal of Multimedia Data Engineering and Management (IJMDEM), 7(1), 22-40. http://doi.org/10.4018/IJMDEM.2016010102

Chicago

Zolghadr, Esfandiar, and Borko Furht. "Context-Based Scene Understanding," International Journal of Multimedia Data Engineering and Management (IJMDEM) 7, no.1: 22-40. http://doi.org/10.4018/IJMDEM.2016010102

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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.

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