Learning a Generative Model for Structural Representations

Abstract. Graph-based representations have been used with considercess in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nodes and edges is governed by a set of independent Bernoulli trials with parameters to be estimated from data in a situation where the correspondences between the nodes in the data graphs and the nodes in the model are not known ab initio and must be estimated from local structure. This results in an EM-like approach where we alternate the estimation of the node correspondences with the estimation of the model parameters. The former estimation is cast as an instance of graph matching, while the latter estimation, together...

• Type: Conference
• Year: 2008
• Where: AUSAI
• Authors: Andrea Torsello, David L. Dowe
• Keywords: Archetypal Structural Models, Artificial Intelligence, AUSAI 2008, Graph-based Representations, Independent Bernoulli Trials