Query Range Sensitive Probability Guided Multi-probe Locality Sensitive Hashing

Xiaoguang Gu, Lei Zhang, Dongming Zhang, Yongdong Zhang, Jintao Li, Ning Bao

Abstract: Locality Sensitive Hashing (LSH) is proposed to construct indexes for high-dimensional approximate similarity search. Multi-Probe LSH (MPLSH) is a variation of LSH which can reduce the number of hash tables. Based on the idea of MPLSH, this paper proposes a novel probability model and a query-adaptive algorithm to generate the optimal multi-probe sequence for range queries. Our probability model takes the query range into account to generate the probe sequence which is optimal for range queries. Furthermore, our algorithm does not use a fixed number of probe steps but a query-adaptive threshold to control the search quality. We do the experiments on an open dataset to evaluate our method. The experimental results show that our method can probe fewer points than MPLSH for getting the same recall. As a result, our method can get an average acceleration of 10% compared to MPLSH.