Method for inferring scenes from test images and training data using probability propagation in a markov network

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.

Dependencies are inferred from the claim text.