A More Robust Feature Correspondence for More Accurate Image Recognition

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.