899 research outputs found
Asymmetric Deep Supervised Hashing
Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized
to accomplish similarity search in large-scale multimedia retrieval
applications. Particularly supervised hashing has recently received
considerable research attention by leveraging the label information to preserve
the pairwise similarities of data points in the Hamming space. However, there
still remain two crucial bottlenecks: 1) the learning process of the full
pairwise similarity preservation is computationally unaffordable and unscalable
to deal with big data; 2) the available category information of data are not
well-explored to learn discriminative hash functions. To overcome these
challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH)
framework, which aims to directly embed the transformed semantic information
into the asymmetric similarity approximation and discriminative hashing
function learning. Specifically, a semantic-aware latent embedding is
introduced to asymmetrically preserve the full pairwise similarities while
skillfully handle the cumbersome n times n pairwise similarity matrix.
Meanwhile, a semantic-aware autoencoder is developed to jointly preserve the
data structures in the discriminative latent semantic space and perform data
reconstruction. Moreover, an efficient alternating optimization algorithm is
proposed to solve the resulting discrete optimization problem. Extensive
experimental results on multiple large-scale datasets demonstrate that our
SADIH can clearly outperform the state-of-the-art baselines with the additional
benefit of lower computational costs.Comment: Accepted by The Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval
Several deep supervised hashing techniques have been proposed to allow for
efficiently querying large image databases. However, deep supervised image
hashing techniques are developed, to a great extent, heuristically often
leading to suboptimal results. Contrary to this, we propose an efficient deep
supervised hashing algorithm that optimizes the learned codes using an
information-theoretic measure, the Quadratic Mutual Information (QMI). The
proposed method is adapted to the needs of large-scale hashing and information
retrieval leading to a novel information-theoretic measure, the Quadratic
Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness
of the proposed method under different scenarios and outperforming existing
state-of-the-art image hashing techniques, this paper provides a structured way
to model the process of information retrieval and develop novel methods adapted
to the needs of each application
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Hashing techniques are in great demand for a wide range of real-world
applications such as image retrieval and network compression. Nevertheless,
existing approaches could hardly guarantee a satisfactory performance with the
extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss
and the shrink of the discrete solution space. In this paper, we propose a
novel \textit{Collaborative Learning} strategy that is tailored for generating
high-quality low-bit hash codes. The core idea is to jointly distill
bit-specific and informative representations for a group of pre-defined code
lengths. The learning of short hash codes among the group can benefit from the
manifold shared with other long codes, where multiple views from different hash
codes provide the supplementary guidance and regularization, making the
convergence faster and more stable. To achieve that, an asymmetric hashing
framework with two variants of multi-head embedding structures is derived,
termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of
training and querying. Extensive experiments on three benchmark datasets have
been conducted to verify the superiority of the proposed MAH, and have shown
that the 8-bit hash codes generated by MAH achieve of the MAP (Mean
Average Precision (MAP)) score on the CIFAR-10 dataset, which significantly
surpasses the performance of the 48-bit codes by the state-of-the-arts in image
retrieval tasks
Dual Asymmetric Deep Hashing Learning
Due to the impressive learning power, deep learning has achieved a remarkable
performance in supervised hash function learning. In this paper, we propose a
novel asymmetric supervised deep hashing method to preserve the semantic
structure among different categories and generate the binary codes
simultaneously. Specifically, two asymmetric deep networks are constructed to
reveal the similarity between each pair of images according to their semantic
labels. The deep hash functions are then learned through two networks by
minimizing the gap between the learned features and discrete codes.
Furthermore, since the binary codes in the Hamming space also should keep the
semantic affinity existing in the original space, another asymmetric pairwise
loss is introduced to capture the similarity between the binary codes and
real-value features. This asymmetric loss not only improves the retrieval
performance, but also contributes to a quick convergence at the training phase.
By taking advantage of the two-stream deep structures and two types of
asymmetric pairwise functions, an alternating algorithm is designed to optimize
the deep features and high-quality binary codes efficiently. Experimental
results on three real-world datasets substantiate the effectiveness and
superiority of our approach as compared with state-of-the-art.Comment: 12 pages, 6 figures, 7 tables, 37 conference
A Survey on Learning to Hash
Nearest neighbor search is a problem of finding the data points from the
database such that the distances from them to the query point are the smallest.
Learning to hash is one of the major solutions to this problem and has been
widely studied recently. In this paper, we present a comprehensive survey of
the learning to hash algorithms, categorize them according to the manners of
preserving the similarities into: pairwise similarity preserving, multiwise
similarity preserving, implicit similarity preserving, as well as quantization,
and discuss their relations. We separate quantization from pairwise similarity
preserving as the objective function is very different though quantization, as
we show, can be derived from preserving the pairwise similarities. In addition,
we present the evaluation protocols, and the general performance analysis, and
point out that the quantization algorithms perform superiorly in terms of
search accuracy, search time cost, and space cost. Finally, we introduce a few
emerging topics.Comment: To appear in IEEE Transactions On Pattern Analysis and Machine
Intelligence (TPAMI
Deep Policy Hashing Network with Listwise Supervision
Deep-networks-based hashing has become a leading approach for large-scale
image retrieval, which learns a similarity-preserving network to map similar
images to nearby hash codes. The pairwise and triplet losses are two widely
used similarity preserving manners for deep hashing. These manners ignore the
fact that hashing is a prediction task on the list of binary codes. However,
learning deep hashing with listwise supervision is challenging in 1) how to
obtain the rank list of whole training set when the batch size of the deep
network is always small and 2) how to utilize the listwise supervision. In this
paper, we present a novel deep policy hashing architecture with two systems are
learned in parallel: a query network and a shared and slowly changing database
network. The following three steps are repeated until convergence: 1) the
database network encodes all training samples into binary codes to obtain a
whole rank list, 2) the query network is trained based on policy learning to
maximize a reward that indicates the performance of the whole ranking list of
binary codes, e.g., mean average precision (MAP), and 3) the database network
is updated as the query network. Extensive evaluations on several benchmark
datasets show that the proposed method brings substantial improvements over
state-of-the-art hashing methods.Comment: 8 pages, accepted by ACM ICM
Fusion Hashing: A General Framework for Self-improvement of Hashing
Hashing has been widely used for efficient similarity search based on its
query and storage efficiency. To obtain better precision, most studies focus on
designing different objective functions with different constraints or penalty
terms that consider neighborhood information. In this paper, in contrast to
existing hashing methods, we propose a novel generalized framework called
fusion hashing (FH) to improve the precision of existing hashing methods
without adding new constraints or penalty terms. In the proposed FH, given an
existing hashing method, we first execute it several times to get several
different hash codes for a set of training samples. We then propose two novel
fusion strategies that combine these different hash codes into one set of final
hash codes. Based on the final hash codes, we learn a simple linear hash
function for the samples that can significantly improve model precision. In
general, the proposed FH can be adopted in existing hashing method and achieve
more precise and stable performance compared to the original hashing method
with little extra expenditure in terms of time and space. Extensive experiments
were performed based on three benchmark datasets and the results demonstrate
the superior performance of the proposed frameworkComment: 10 pages, 6 figure
End-to-End Supervised Product Quantization for Image Search and Retrieval
Product Quantization, a dictionary based hashing method, is one of the
leading unsupervised hashing techniques. While it ignores the labels, it
harnesses the features to construct look up tables that can approximate the
feature space. In recent years, several works have achieved state of the art
results on hashing benchmarks by learning binary representations in a
supervised manner. This work presents Deep Product Quantization (DPQ), a
technique that leads to more accurate retrieval and classification than the
latest state of the art methods, while having similar computational complexity
and memory footprint as the Product Quantization method. To our knowledge, this
is the first work to introduce a dictionary-based representation that is
inspired by Product Quantization and which is learned end-to-end, and thus
benefits from the supervised signal. DPQ explicitly learns soft and hard
representations to enable an efficient and accurate asymmetric search, by using
a straight-through estimator. Our method obtains state of the art results on an
extensive array of retrieval and classification experiments
Learning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area
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