Fast approximate nearest-neighbor field by Cascaded Spherical hashing

Abstract

We present an efficient and fast algorithm for computing approximate nearest neighbor fields between two images. Our method builds on the concept of Coherency-Sensitive Hashing (CSH), but uses a recent hashing scheme, Spherical Hashing (SpH), which is known to be better adapted to the nearest-neighbor problem for natural images. Cascaded Spherical Hashing concatenates different configurations of SpH to build larger Hash Tables with less elements in each bin to achieve higher selectivity. Our method is able to amply outperform existing techniques like PatchMatch and CSH. The parallelizable scheme has been straightforwardly implemented in OpenCL, and the experimental results show that our algorithm is faster and more accurate than existing methods.