Well this datastructure lets us find the k nearest neighbors in Hamming space (think binary vectors in binary vector space) quickly. If we can map features (for

Well this datastructure lets us find the k nearest neighbors in Hamming space (think binary vectors in binary vector space) quickly. If we can map features (for simplicity lets think of images) to points in hamming space such that similar features have a small hamming distance then we have a fast way of finding similar images. That is the problem that this paper proposes to help with, but I could easily see this also applying to sound clips or other media, though my imagination is not that great. When I used this code I applied it to the problem of image matching using binary codes generated by a feature detector [1]. The benefit of using binary codes over traditional floating point vectors is they are much faster to compare. [1] http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html#orb http://docs.opencv.org/modules/features2d/doc/feature_detect...)