In this paper, a novel algorithm for finding the
optimal correspondence between two sets of image features has
been introduced. The proposed algorithm pays attention not
only to the similarity between features but also to the spatial
layout of every matched feature and its neighbors. Unlike
related methods that use geometrical relations between the
neighboring features, the proposed method employes topology
that survives against different types of deformations like
scaling and rotation; resulting in more robust matching. The
features are expressed as an undirected graph where every
node represents a local feature and every edge represents
adjacency between them. The topology of the resulting graph
can be considered as a robust global feature of the represented
object. The matching process is modeled as a graph matching
problem; which in turn is formulated as a variation of the
quadratic assignment problem. In this variation, a number
of parameters are used to control the significance of global vs.
local features to tune the performance and customize the model.
The experimental results show a significant improvement in
the number of correct matches using the proposed method
compared to different methods.