Local Sensitive Hashing for Proximity Searching

Abstract

Proximity or similarity searching is one of the most important tasks in artificial intelligence concerning multimedia databases. If there is a distance function to compare any two objects in a collection, then similarity can be modeled as a metric space. One of the most important techniques used for high dimensional data is the permutation-based algorithm, where the problem is mapped into another space (permutations space) where distances are much easier to compute, but solving similarity queries with the least number of distances computed is still a challenge. The approach in this work consists in using Locality-Sensitive Hashing (LSH). The experiments reported in this paper show that the proposed way to adapt LSH to the permutation based algorithm has a competitive tradeoff between recall and distances.