Paper Info
Title
A Game- Theoretic Hyper-Graph Matching Algorithm.
Abstract
Feature matching is aimed to establish the correspondences between features of two sets. Aside from the well-known graph matching, hyper-graph matching is receiving increasing interests due to its ability to encode more invariance information. Existing hyper-graph matching algorithms are usually based on the maximization of matching score between correspondences. In this paper we treat the candidate matches as pure strategies and formulate the hyper-graph matching problem as a non-cooperative multi-player clustering game. Specifically, we calculate the higher-order similarity as the payoff of players in selecting the corresponding triplet of pure strategies, and find that the subset of consistent matches can be extracted by optimizing a polynomial function with a higher-order replicator dynamics over the standard simplex. With the Baum-Eagon inequality, we arrive at the equilibrium of the game and obtain a subset of consistent matches as the final matching result. Our approach is especially useful in dealing with the case that some features in the model image have no correspondences in the test image. In addition, with our approach each match is assigned a weight which reflects the relationship with other matches and can be used to enforce the one-to-one constraint. Experiments on both synthetic datasets and real images demonstrate the effectiveness of our approach.
Year
DOI
Venue
2018
10.1109/ICPR.2018.8545827
ICPR
Field
DocType
Citations 
Pattern recognition,Computer science,Replicator equation,Algorithm,Matching (graph theory),Artificial intelligence,Real image,Cluster analysis,Nash equilibrium,Pattern matching,Standard test image,Stochastic game
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jian Hou112617.11
Marcello Pelillo21888150.33