18 research outputs found
Efficient Querying from Weighted Binary Codes
Binary codes are widely used to represent the data due to their small storage
and efficient computation. However, there exists an ambiguity problem that lots
of binary codes share the same Hamming distance to a query. To alleviate the
ambiguity problem, weighted binary codes assign different weights to each bit
of binary codes and compare the binary codes by the weighted Hamming distance.
Till now, performing the querying from the weighted binary codes efficiently is
still an open issue. In this paper, we propose a new method to rank the
weighted binary codes and return the nearest weighted binary codes of the query
efficiently. In our method, based on the multi-index hash tables, two
algorithms, the table bucket finding algorithm and the table merging algorithm,
are proposed to select the nearest weighted binary codes of the query in a
non-exhaustive and accurate way. The proposed algorithms are justified by
proving their theoretic properties. The experiments on three large-scale
datasets validate both the search efficiency and the search accuracy of our
method. Especially for the number of weighted binary codes up to one billion,
our method shows a great improvement of more than 1000 times faster than the
linear scan.Comment: 13 pages, accepted by AAAI202
GGNN: Graph-based GPU Nearest Neighbor Search
Approximate nearest neighbor (ANN) search in high dimensions is an integral
part of several computer vision systems and gains importance in deep learning
with explicit memory representations. Since PQT and FAISS started to leverage
the massive parallelism offered by GPUs, GPU-based implementations are a
crucial resource for today's state-of-the-art ANN methods. While most of these
methods allow for faster queries, less emphasis is devoted to accelerate the
construction of the underlying index structures. In this paper, we propose a
novel search structure based on nearest neighbor graphs and information
propagation on graphs. Our method is designed to take advantage of GPU
architectures to accelerate the hierarchical building of the index structure
and for performing the query. Empirical evaluation shows that GGNN
significantly surpasses the state-of-the-art GPU- and CPU-based systems in
terms of build-time, accuracy and search speed