Abstract
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 multiobject matching techniques