---
res:
  bibo_abstract:
  - "In this paper, we present a new approach for establishing correspondences between
    sparse image features related by an unknown nonrigid mapping and corrupted by
    clutter and occlusion, such as points extracted from images of different instances
    of the same object category. We formulate this matching task as an energy minimization
    problem by defining an elaborate 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 an 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.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Lorenzo
      foaf_name: Torresani, Lorenzo
      foaf_surname: Torresani
  - foaf_Person:
      foaf_givenName: Vladimir
      foaf_name: Kolmogorov, Vladimir
      foaf_surname: Kolmogorov
      foaf_workInfoHomepage: http://www.librecat.org/personId=3D50B0BA-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Carsten
      foaf_name: Rother, Carsten
      foaf_surname: Rother
  bibo_doi: 10.1109/TPAMI.2012.105
  bibo_issue: '2'
  bibo_volume: 35
  dct_date: 2012^xs_gYear
  dct_identifier:
  - UT:000312560600002
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: A dual decomposition approach to feature correspondence@
...