Adaptive bit allocation hashing for approximate nearest neighbor search

Abstract

Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attentions. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding. One problem of these methods is that every dimension of the projected data is regarded as the same importance and represented by one bit, which may result in ineffective codes. We introduce an adaptive bit allocation hashing (ABAH) method to encode data for ANN search. The basic idea is, according to the dispersion of every dimension after projection we use different number of bits to encode them. ABAH can effectively preserve the neighborhood structure in the original data space. Extensive experiments show that ABAH significantly outperforms three state-of-the-art methods.