Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way.
In this paper, we regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables.
The construction method is general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings.
Extensive experiments on two large-scale benchmarks demonstrate that the proposed method outperforms both naive construction method and state-of-the-art hashing algorithms, with up to 65.93% accuracy gains.