Improved Sparse Correspondence Resolution using Loopy Belief Propagation with MRF Clique based Structure Preservation

M. Louw and F. Nicolls (South Africa)


Feature matching, MRF, LBP, sparse correspondence.


In this paper we develop a novel MRF formulation for calculating sparse features correspondence in image pairs. Our MRF terms can include cliques of variable sizes, and solve these using Loopy Belief Propagation. To calculate our MRF topology we develop a variant of the K-means algorithm which we call the KN-means algorithm (where each mean has a specified number of neighbours). The method is compared to other state of the art sparse feature correspondence algorithms and shown to compare well, es pecially for less dense feature sets. Outliers are handled naturally within this paradigm.

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