1 research outputs found
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 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