Stochastic grammars for images on arbitrary graphs

Abstract

We describe a class of multiscale stochastic processes based on stochastic context-free grammars and called spatial random trees (SRTs) which can be effectively used for modeling multidimensional signals. In addition to modeling images which are sampled on a regular rectangular grid, we generalize this methodology to images defined on arbitraly graph structures. We develop likelihood calculation, MAP estimation, and EM-based parameter estimation algorithms for SRTs. To illustrate these methods, we apply them to classification of natural images using region graphs extracted by a recursive bipartitioning segmentation algorithm. 407

Inferência de gramáticas estocásticas para reconhecimento de padrões de imagens utilizando quadtrees

“…e[34] o modelo probabilístico hierárquico SRT. A principal contribuição desses trabalhosé a introdução de classes de distribuição de probabilidades sobre asárvores para cálculos exatos da verossimilhança em tempo polinomial.…”