Abstract

TreeCANN is a fast algorithm for approximately matching all patches between two images. It does so by following the established convention of finding an initial set of matching patch candidates be- tween the two images and then propagating good matches to neighboring patches in the image plane. TreeCANN accelerates each of these compo- nents substantially leading to an algorithm that is ? to ? faster than existing methods. Seed matching is achieved using a properly tuned k-d tree on a sparse grid of patches. In particular, we show that a sequence of key design decisions can make k-d trees run as fast as recently proposed state-of-the-art methods, and because of image coherency it is enough to consider only a sparse grid of patches across the image plane. We then develop a novel propagation step that is based on the integral image, which drastically reduces the computational load that is dominated by the need to repeatedly measure similarity between pairs of patches. As a by-product we give an optimal algorithm for exact matching that is based on the integral image. The proposed exact algorithm is faster than previously reported results and depends only on the size of the images and not on the size of the patches. We report results on large and var- ied data sets and show that TreeCANN is orders of magnitude faster than exact NN search yet produces matches that are within 1% error, compared to the exact NN search.