Modeling and estimation of spatial random trees with application to image classification

A new class of multiscale multidimensional stochastic processes called spatial random trees is introduced. The model is based on multiscale stochastic trees with stochastic structure as well as stochastic states. Procedures are developed for exact likelihood calculation, MAP estimation of the process, and estimation of the parameters of the process. The new framework is illustrated through a simple binary image classification problem.