  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 to handle practical problems with thousands of features.
Both simulation and real experiments demonstrate that the proposed algorithm
can achieve a competitive performance with an order of magnitude speedup
compared to the state-of-the-art algorithm. In the end, we demonstrate the
applicability of the proposed method to match the images of different object
instances and as a result the potential to reconstruct category-specific object
models from those images.
