@conference {Torresani2008,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	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. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Torresani2008a,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	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. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
