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We propose a novel hashing-based matching scheme, called Locally OptimizedHashing (LOH), based on a state-of-the-art quantization algorithm that can beused for efficient, large-scale search, recommendation, clustering, anddeduplication. We show that matching with LOH only requires set intersectionsand summations to compute and so is easily implemented in generic distributedcomputing systems. We further show application of LOH to: a) large-scale searchtasks where performance is on par with other state-of-the-art hashingapproaches; b) large-scale recommendation where queries consisting of thousandsof images can be used to generate accurate recommendations from collections ofhundreds of millions of images; and c) efficient clustering with a graph-basedalgorithm that can be scaled to massive collections in a distributedenvironment or can be used for deduplication for small collections, like searchresults, performing better than traditional hashing approaches while onlyrequiring a few milliseconds to run. In this paper we experiment on datasets ofup to 100 million images, but in practice our system can scale to largercollections and can be used for other types of data that have a vectorrepresentation in a Euclidean space.

LOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing