Holistic Context Modeling using Semantic Co-occurrences

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Holistic Context Modeling using Semantic Co-occurrences
We present a simple framework to model contextual relationships between visual concepts. The new framework combines ideas from previous object-centric methods (which model contextual relationships between objects in an image, such as their co-occurrence patterns) and scenecentric methods (which learn a holistic context model from the entire image, known as its “gist”). This is accomplished without demarcating individual concepts or regions in the image. First, using the output of a generic appearance based concept detection system, a semantic space is formulated, where each axis represents a semantic feature. Next, context models are learned for each of the concepts in the semantic space, using mixtures of Dirichlet distributions. Finally, an image is represented as a vector of posterior concept probabilities under these contextual concept models. It is shown that these posterior probabilities are remarkably noise-free, and an effective model of the contextual rela...
Nikhil Rasiwasia (University Of California, San Di
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Nikhil Rasiwasia (University Of California, San Diego), Nuno Vasconcelos (University of California, San Diego)
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