In this paper we propose a global optimization-based approach to jointly matching a set of
images. The estimated correspondences simultaneously maximize pairwise feature affinities
and cycle consistency across multiple images. Unlike previous convex methods relying on
semidefinite programming, we formulate the problem as a low-rank matrix recovery problem
and show that the desired semidefiniteness of a solution can be spontaneously fulfilled. The
low-rank formulation enables us to derive a fast alternating minimization algorithm in order …

In this paper we propose a global optimization-based approach to jointly matching a set of
images. The estimated correspondences simultaneously maximize pairwise feature affinities
and cycle consistency across multiple images. Unlike previous convex methods relying on
semidefinite programming, we formulate the problem as a low-rank matrix recovery problem
and show that the desired semidefiniteness of a solution can be spontaneously fulfilled. The
low-rank formulation enables us to derive a fast alternating minimization algorithm in order …