Feature Correspondence Via Graph Matching: Models and Global Optimization

Abstract. In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show ...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Lorenzo Torresani, Vladimir Kolmogorov, Carsten Rother
• Keywords: Complex Objective Function, Computer Vision, ECCV 2008, Energy Minimization Problem, Graph Matching Algorithms, Graph Matching Optimization, Unknown Non-rigid Mapping