Abstract: A method infers a scene from a test image. During a training phase, a plurality of images and corresponding scenes are acquired. Each of the images and corresponding scenes are partitioned respectively into a plurality of image patches and scene patches. Each image patch is represented as an image vector, and each scene patch is represented as a scene vector. The image vectors and scene vectors are modeled as a network. During an inference phase, the test image is acquired. The test image is partitioned into a plurality of test image patches. Each test image patch is represented as a test image vector. Candidate scene vectors corresponding to the test image vectors are located in the network. Compatibility matrices for the candidate scene vectors are determined, and probabilities of the compatibility matrices are propagated in the network until convergence to infer the scene from the test image.

Abstract: A method for estimating scenes from images generates a plurality of scenes and renders an image for each scene. The scenes and corresponding images are partitioned into patches. Each patch is quantitized as a vector and probability density function is fitted to each vector. The patches and probabilities are organized as a Markov network where local probability information is propagated to neighboring nodes. After propagation, the probability densities at each node is used to estimate the scene.

Abstract: A method for estimating scenes from images generates a plurality of scenes and renders an image for each scene. The scenes and corresponding images are partitioned into patches. Each patch is quantitized as a vector and probability density function is fitted to each vector. The patches and probability densities are organized as a Markov network where local probability information is propagated to neighboring nodes. After propagation, the probability density at each node is used to estimate the scene.