Computing nearest-neighbor fields via Propagation-Assisted KD-Trees

Matching patches between two images, also known as computing nearest-neighbor fields, has been proven a useful technique in various computer vision/graphics algorithms. But this is a computationally challenging nearest-neighbor search task, because both the query set and the candidate set are of image size. In this paper, we propose Propagation-Assisted KD-Trees to quickly compute an approximate solution. We develop a novel propagation search method for kd-trees. In this method the tree nodes checked by each query are propagated from the nearby queries. This method not only avoids the time-consuming backtracking in traditional tree methods, but is more accurate. Experiments on public data show that our method is 10-20 times faster than the PatchMatch method 4 at the same accuracy, or reduces its error by 70% at the same running time. Our method is also 2-5 times faster and is more accurate than Coherency Sensitive Hashing 22, a latest state-of-the-art method.