  In this paper, we consider the problem of finding the feature correspondences
among a collection of feature sets, by using their point-wise unary features.
This is a fundamental problem in computer vision and pattern recognition, which
also closely relates to other areas such as operational research. Different
from two-set matching which can be transformed to a quadratic assignment
programming task that is known NP-hard, inclusion of merely unary attributes
leads to a linear assignment problem for matching two feature sets. This
problem has been well studied and there are effective polynomial global optimum
solvers such as the Hungarian method. However, it becomes ill-posed when the
unary attributes are (heavily) corrupted. The global optimal correspondence
concerning the best score defined by the attribute affinity/cost between the
two sets can be distinct to the ground truth correspondence since the score
function is biased by noises. To combat this issue, we devise a method for
matching a collection of feature sets by synergetically exploring the
information across the sets. In general, our method can be perceived from a
(constrained) clustering perspective: in each iteration, it assigns the
features of one set to the clusters formed by the rest of feature sets, and
updates the cluster centers in turn. Results on both synthetic data and real
images suggest the efficacy of our method against state-of-the-arts.
