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Feature Correspondence via Graph Matching: Models and Global Optimization
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@MISC{Torresani_featurecorrespondence,
author = {Lorenzo Torresani and Vladimir Kolmogorov and Carsten Rother},
title = {Feature Correspondence via Graph Matching: Models and Global Optimization},
year = {}
}
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Abstract
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 on several matching tasks that our learned model yields results superior to those of state-of-the-art methods. 1
Keyphrases
graph matching global optimization feature correspondence dual decomposition distinct pose global minimum example dd human figure spatial arrangement np-hard problem sparse image feature state-of-the-art method new approach optimization technique unknown non-rigid mapping complex objective function learned model yield result energy minimization problem novel graph
