Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures

Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism problem to generalise a model graph over a set of training examples. Then a full probabilistic model is assigned to the learnt graph. We call this approach Probabilistic Structure Graphs (PSGs). This article explains how PSG models are learnt and how probabilities for model instances are derived. It shows how to use PSG models to perform MAP classification, and presents evaluation of learnt models in the context of image understanding. Here, we classify observable object structures in the domain of building facade images (average classification rate 80%). Additionally, we present encouraging results from interpreting facade images, where we detect instances of learnt models in a set of cluttered objects. We show that bottom-up scene interpretation based solely on learnt models seems achievable, without any hand...

• Type: Journal
• Year: 2009
• Where: ICMLA
• Authors: Johannes Hartz
• Keywords: Facade Images, ICMLA 2009, Learnt Models, Machine Learning, PSG Models