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...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Nikhil Rasiwasia (University Of California, San Diego), Nuno Vasconcelos (University of California, San Diego)
• Keywords: Computer Vision, CVPR 2009, Holistic Context Modeling, Semantic Co-occurrences