Fan, Bin; Kong, Qingqun; Zhang, Baoqian; Liu, Hongmin; Pan, Chunhong; Lu, Jiwen
Abstract
Fast approximate nearest neighbor search has been well studied for real-valued vectors, however, the methods for binary descriptors are less developed. The paper addresses this problem by resorting to the well established techniques in Euclidean space. To this end, the binary descriptors are firstly mapped into low dimensional float vectors under the condition that the neighborhood information in the original Hamming space could be preserved in the mapped Euclidean space as much as possible. Then, KD-Tree is used to partitioning the mapped Euclidean space in order to quickly find approximate nearest neighbors for a given query point. This is identical to filter out a subset of nearest neighbor candidates in the original Hamming space due to the property of neighborhood preserving. Finally, Hamming ranking is applied to the small number of candidates to find out the approximate nearest neighbor in the original Hamming space, with only a fraction of running time compared to the bruteforce linear scan. Our experiments demonstrate that the proposed method significantly outperforms the state of the arts, obtaining improved search accuracy at various speed up factors, e.g., at least 16% improvement of search accuracy over previous methods (from 67.7% to 83.7%) when the search speed is 200 times faster than the linear scan for a one million database.
Publisher
Pattern Recognition
Research Area
Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic