An object recognition model based on visual grammars and bayesian networks

Abstract

A novel proposal for a general model for object recognition is presented. The proposed method is based on symbol-relational grammars and Bayesian networks. An object is modeled as a hierarchy of features and spatial relationships using a symbol-relational grammar. This grammar is learned automatically from examples, incorporating a simple segmentation algorithm in order to generate the lexicon. The grammar is created with the elements of the lexicon as terminal elements. This representation is automatically transformed into a Bayesian network structure which parameters are learned from examples. Thus, recognition is based on probabilistic inference in the Bayesian network representation. Preliminary results in modeling natural objects are presented. The main contribution of this work is a general methodology for building object recognition systems which combines the expressivity of a grammar with the robustness of probabilistic inference. © 2014 Springer-Verlag Berlin Heidelberg.