786 research outputs found
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Thanks to the efficient retrieval speed and low storage consumption, learning
to hash has been widely used in visual retrieval tasks. However, existing
hashing methods assume that the query and retrieval samples lie in homogeneous
feature space within the same domain. As a result, they cannot be directly
applied to heterogeneous cross-domain retrieval. In this paper, we propose a
Generalized Image Transfer Retrieval (GITR) problem, which encounters two
crucial bottlenecks: 1) the query and retrieval samples may come from different
domains, leading to an inevitable {domain distribution gap}; 2) the features of
the two domains may be heterogeneous or misaligned, bringing up an additional
{feature gap}. To address the GITR problem, we propose an Asymmetric Transfer
Hashing (ATH) framework with its unsupervised/semi-supervised/supervised
realizations. Specifically, ATH characterizes the domain distribution gap by
the discrepancy between two asymmetric hash functions, and minimizes the
feature gap with the help of a novel adaptive bipartite graph constructed on
cross-domain data. By jointly optimizing asymmetric hash functions and the
bipartite graph, not only can knowledge transfer be achieved but information
loss caused by feature alignment can also be avoided. Meanwhile, to alleviate
negative transfer, the intrinsic geometrical structure of single-domain data is
preserved by involving a domain affinity graph. Extensive experiments on both
single-domain and cross-domain benchmarks under different GITR subtasks
indicate the superiority of our ATH method in comparison with the
state-of-the-art hashing methods
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