For a point-based image registration method, point matching is a hard and a computationally intensive task to handle especially when issues of noisy and outlying data have to be considered. In this paper we cast the problem as a combinatorial optimization task and we describe a global optimization method to achieve robust point matching and pose estimation for image registration purpose. The basic idea is to use Ant Colony System (ACS) as a population based search strategy to evolve promising starting solutions i.e affine transformations. An appropriate local search inspired from extremal optimization is developed and embedded within the search strategy to refine the solutions found. Experimental results are very promising and show the ability of the method to cope with outliers and to achieve robust pose estimation.