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Efficient Querying from Weighted Binary Codes
Binary codes are widely used to represent the data due to their small st...
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Polysemous codes
This paper considers the problem of approximate nearest neighbor search ...
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The Power of Asymmetry in Binary Hashing
When approximating binary similarity using the hamming distance between ...
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Empowering Elasticsearch with Exact and Fast r-Neighbor Search in Hamming Space
A growing interest has been witnessed recently in building nearest neigh...
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Fast Exact Search in Hamming Space with Multi-Index Hashing
There is growing interest in representing image data and feature descrip...
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Hardness of Approximate Nearest Neighbor Search
We prove conditional near-quadratic running time lower bounds for approx...
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Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification
Similarity search is essential to many important applications and often ...
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Accelerating Search on Binary Codes in Weighted Hamming Space
Compared to Hamming distance, weighted Hamming distance as a similarity measure between binary codes and the binary query point can provide superior accuracy in the search tasks. However, how to efficiently find K binary codes in the dataset that have the smallest weighted Hamming distance with the query is still an open issue. In this paper, a non-exhaustive search framework is proposed to accelerate the search speed and guarantee the search accuracy on the binary codes in weighted Hamming space. By separating the binary codes into multiple disjoint substrings as the bucket indices, the search framework iteratively probes the buckets until the query's nearest neighbors are found. The framework consists of two modules, the search module and the decision module. The search module successively probes the buckets and takes the candidates according to a proper probing sequence generated by the proposed search algorithm. And the decision module decides whether the query's nearest neighbors are found or more buckets should be probed according to a designed decision criterion. The analysis and experiments indicate that the search framework can solve the nearest neighbor search problem in weighted Hamming space and is orders of magnitude faster than the linear scan baseline.
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