TY - JOUR
T1 - Graphical models for graph matching
T2 - Approximate models and optimal algorithms
AU - Caelli, Terry
AU - Caetano, Tiberio
PY - 2005/2
Y1 - 2005/2
N2 - Comparing scene, pattern or object models to structures in images or determining the correspondence between two point sets are examples of attributed graph matching. In this paper we show how such problems can be posed as one of inference over hidden Markov random fields. We review some well known inference methods studied over past decades and show how the Junction Tree framework from Graphical Models leads to algorithms that outperform traditional relaxation-based ones.
AB - Comparing scene, pattern or object models to structures in images or determining the correspondence between two point sets are examples of attributed graph matching. In this paper we show how such problems can be posed as one of inference over hidden Markov random fields. We review some well known inference methods studied over past decades and show how the Junction Tree framework from Graphical Models leads to algorithms that outperform traditional relaxation-based ones.
KW - Attributed graph matching
KW - Hidden Markov random fields
KW - Junction Tree algorithms
KW - Relaxation labeling
UR - http://www.scopus.com/inward/record.url?scp=12344265781&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2004.10.022
DO - 10.1016/j.patrec.2004.10.022
M3 - Article
SN - 0167-8655
VL - 26
SP - 339
EP - 346
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 3
ER -