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.