LOH and Behold: Web-Scale Visual Search, Recommendation and Clustering Using Locally Optimized Hashing

@article{Kalantidis2016LOHAB,
  title={LOH and Behold: Web-Scale Visual Search, Recommendation and Clustering Using Locally Optimized Hashing},
  author={Yannis Kalantidis and Lyndon S. Kennedy and Huy Nguyen and Clayton Mellina and David A. Shamma},
  journal={ArXiv},
  year={2016},
  volume={abs/1604.06480}
}
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… 

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