Spatial random trees with applications to image classification

We develop a new methodology for constructing hierarchical stochastic image models called spatial random trees (SRTs) which admit polynomial-complexity exact inference algorithms. We use our framework of multitree dictionaries as the starting point for this construction. We develop an efficient algorithm for computing the EM updates and use it to estimate the model parameters. We illustrate our models and algorithms through image classification experiments.