We propose a novel hashing-based matching scheme, called Locally Optimized
Hashing (LOH), based on a state-of-the-art quantization algorithm that can be
used for efficient, large-scale search, recommendation, clustering, and
deduplication. We show that matching with LOH only requires set intersections
and summations to compute and so is easily implemented in generic distributed
computing systems. We further show application of LOH to: a) large-scale search
tasks where performance is on par with other state-of-the-art hashing
approaches; b) large-scale recommendation where queries consisting of thousands
of images can be used to generate accurate recommendations from collections of
hundreds of millions of images; and c) efficient clustering with a graph-based
algorithm that can be scaled to massive collections in a distributed
environment or can be used for deduplication for small collections, like search
results, performing better than traditional hashing approaches while only
requiring a few milliseconds to run. In this paper we experiment on datasets of
up to 100 million images, but in practice our system can scale to larger
collections and can be used for other types of data that have a vector
representation in a Euclidean space.

本文提出了一种新的哈希相似匹配方案 —— 局部优化哈希（LOH），该方案是基于一种最先进的量化算法的。LOH 可用于高效的大规模搜索、推荐、聚类和去重。我们进一步展示了 LOH 的应用：a）大规模搜索任务中，性能与其他最先进的哈希方式相当；b）大规模推荐中，可使用由数千幅图像组成的查询，从数亿图像的集合中生成准确的推荐；c）利用基于图形的算法进行高效聚类，该算法可在分布式环境中扩展到海量收藏品，或用于小集合的去重，如搜索结果，其性能优于传统哈希方案，仅需几毫秒运行。在本文中，我们在最多 1 亿张图片的数据集上进行实验，但实际上我们的系统可以扩展到更大的收藏品，并可用于在欧几里得空间中具有向量表示的其他类型的数据。