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 scene-centric 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 relationships between semantic concepts in natural images. This is further demonstrated through an experimental evaluation with respect to two vision tasks, viz. scene classification and image annotation, on benchmark datasets. The results show that, besides quite simple to compute, the proposed context models attain superior performance than state of the art systems in both tasks.