Feature matching in growing databases

As feature-based image matching is applied to increasing larger scale problems, it becomes necessary to match features across increasingly larger databases. Current approaches are able to conduct such feature matching, but are not flexible enough to be applied to databases that may grow at runtime. As a solution to this problem, we present the Iterative k-d tree that allows for the insertion of new features into the database at any time and stores information about previous queries so that previously searched features can updated without having to be re-run. This new data structure was successfully used in the Spry algorithm to achieve better and faster results in situations where there is large movement between images. Additionally, experimental results show that the proposed method is significantly faster than the current state of the art algorithms when the database of features grows at runtime.