A fast binary encoding mechanism for approximate nearest neighbor search

In this paper, a novel approach which can map high-dimensional, real-valued data into low-dimensional, binary vectors is proposed to achieve fast approximate nearest neighbor (ANN) search. In our paper, the binary codes are required to preserve the relative similarity, which makes the Hamming distances of data pairs approximate their Euclidean distances in ANN search. Under such constraint, the distribution adaptive binary labels are obtained through a lookup-based mechanism. The perpendicular bisector planes located between two kinds of data whose binary labels are different on only one specific bit are considered as weak hash functions. As just two kinds of data are taken into consideration during generation of the weak hash functions, the final strong hash functions are formed by combining the weak ones through boosting scheme to map all kinds of data into binary codes effectively. Experimental results show that our algorithm can encode the out of samples efficiently, and the performances of our method are superior to many state-of-the-art methods.