Region-constrained feature matching with hierachical agglomerative clustering

Local feature matching is one of the most fundamental issues in computer vision. Hierarchical agglomerative clustering (HAC) has been effectively used to distinguish inliers from outliers. The drawback of HAC is its large computational complexity which increases rapidly as the number of feature correspondences increases. To overcome this drawback, this paper proposes a region-constrained feature matching in which an image is segmented into small regions and feature correspondences are clustered inside each region. Adjacent segmented regions are merged to form larger regions if the correspondences inside regions are similar. The merge may increase the accuracy of clustering, and consequently, it improves the accuracy of matching operations as well. The proposed region-constrained clustering dramatically reduces the execution time by as much as 500 times compared to the previous clustering while it achieves a similar matching accuracy.