  In this paper we propose an optimization-based framework to multiple object
matching. The framework takes maps computed between pairs of objects as input,
and outputs maps that are consistent among all pairs of objects. The central
idea of our approach is to divide the input object collection into overlapping
sub-collections and enforce map consistency among each sub-collection. This
leads to a distributed formulation, which is scalable to large-scale datasets.
We also present an equivalence condition between this decoupled scheme and the
original scheme. Experiments on both synthetic and real-world datasets show
that our framework is competitive against state-of-the-art multi-object
matching techniques.
