49,928 research outputs found
Deep Discrete Supervised Hashing
Hashing has been widely used for large-scale search due to its low storage
cost and fast query speed. By using supervised information, supervised hashing
can significantly outperform unsupervised hashing. Recently, discrete
supervised hashing and deep hashing are two representative progresses in
supervised hashing. On one hand, hashing is essentially a discrete optimization
problem. Hence, utilizing supervised information to directly guide discrete
(binary) coding procedure can avoid sub-optimal solution and improve the
accuracy. On the other hand, deep hashing, which integrates deep feature
learning and hash-code learning into an end-to-end architecture, can enhance
the feedback between feature learning and hash-code learning. The key in
discrete supervised hashing is to adopt supervised information to directly
guide the discrete coding procedure in hashing. The key in deep hashing is to
adopt the supervised information to directly guide the deep feature learning
procedure. However, there have not existed works which can use the supervised
information to directly guide both discrete coding procedure and deep feature
learning procedure in the same framework. In this paper, we propose a novel
deep hashing method, called deep discrete supervised hashing (DDSH), to address
this problem. DDSH is the first deep hashing method which can utilize
supervised information to directly guide both discrete coding procedure and
deep feature learning procedure, and thus enhance the feedback between these
two important procedures. Experiments on three real datasets show that DDSH can
outperform other state-of-the-art baselines, including both discrete hashing
and deep hashing baselines, for image retrieval
Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing
Deep supervised hashing has become an active topic in information retrieval.
It generates hashing bits by the output neurons of a deep hashing network.
During binary discretization, there often exists much redundancy between
hashing bits that degenerates retrieval performance in terms of both storage
and accuracy. This paper proposes a simple yet effective Neurons Merging Layer
(NMLayer) for deep supervised hashing. A graph is constructed to represent the
redundancy relationship between hashing bits that is used to guide the learning
of a hashing network. Specifically, it is dynamically learned by a novel
mechanism defined in our active and frozen phases. According to the learned
relationship, the NMLayer merges the redundant neurons together to balance the
importance of each output neuron. Moreover, multiple NMLayers are progressively
trained for a deep hashing network to learn a more compact hashing code from a
long redundant code. Extensive experiments on four datasets demonstrate that
our proposed method outperforms state-of-the-art hashing methods.Comment: Accepted by IJCAI 201
Feature Learning based Deep Supervised Hashing with Pairwise Labels
Recent years have witnessed wide application of hashing for large-scale image
retrieval. However, most existing hashing methods are based on hand-crafted
features which might not be optimally compatible with the hashing procedure.
Recently, deep hashing methods have been proposed to perform simultaneous
feature learning and hash-code learning with deep neural networks, which have
shown better performance than traditional hashing methods with hand-crafted
features. Most of these deep hashing methods are supervised whose supervised
information is given with triplet labels. For another common application
scenario with pairwise labels, there have not existed methods for simultaneous
feature learning and hash-code learning. In this paper, we propose a novel deep
hashing method, called deep pairwise-supervised hashing(DPSH), to perform
simultaneous feature learning and hash-code learning for applications with
pairwise labels. Experiments on real datasets show that our DPSH method can
outperform other methods to achieve the state-of-the-art performance in image
retrieval applications.Comment: IJCAI 201
Cross-Modality Hashing with Partial Correspondence
Learning a hashing function for cross-media search is very desirable due to
its low storage cost and fast query speed. However, the data crawled from
Internet cannot always guarantee good correspondence among different modalities
which affects the learning for hashing function. In this paper, we focus on
cross-modal hashing with partially corresponded data. The data without full
correspondence are made in use to enhance the hashing performance. The
experiments on Wiki and NUS-WIDE datasets demonstrates that the proposed method
outperforms some state-of-the-art hashing approaches with fewer correspondence
information
A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval
There is a growing trend in studying deep hashing methods for content-based
image retrieval (CBIR), where hash functions and binary codes are learnt using
deep convolutional neural networks and then the binary codes can be used to do
approximate nearest neighbor (ANN) search. All the existing deep hashing papers
report their methods' superior performance over the traditional hashing methods
according to their experimental results. However, there are serious flaws in
the evaluations of existing deep hashing papers: (1) The datasets they used are
too small and simple to simulate the real CBIR situation. (2) They did not
correctly include the search time in their evaluation criteria, while the
search time is crucial in real CBIR systems. (3) The performance of some
unsupervised hashing algorithms (e.g., LSH) can easily be boosted if one uses
multiple hash tables, which is an important factor should be considered in the
evaluation while most of the deep hashing papers failed to do so.
We re-evaluate several state-of-the-art deep hashing methods with a carefully
designed experimental setting. Empirical results reveal that the performance of
these deep hashing methods are inferior to multi-table IsoH, a very simple
unsupervised hashing method. Thus, the conclusions in all the deep hashing
papers should be carefully re-examined
Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing
In this paper, we propose a ranking based locality sensitive hashing inspired
two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for
biometric template protection. With externally generated random parameters, IoM
hashing transforms a real-valued biometric feature vector into discrete index
(max ranked) hashed code. We demonstrate two realizations from IoM hashing
notion, namely Gaussian Random Projection based and Uniformly Random
Permutation based hashing schemes. The discrete indices representation nature
of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong
concealment to the biometric information. This contributes to the solid ground
of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the
features magnitude, hence is more robust against biometric features variation.
Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes
being scale-invariant, which is critical for matching and feature alignment.
The experimental results demonstrate favorable accuracy performance on
benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its
resilience to the existing and newly introduced security and privacy attacks as
well as satisfy the revocability and unlinkability criteria of cancelable
biometrics.Comment: 15 pages, 8 figures, 6 table
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
Instance-Aware Hashing for Multi-Label Image Retrieval
Similarity-preserving hashing is a commonly used method for nearest neighbour
search in large-scale image retrieval. For image retrieval, deep-networks-based
hashing methods are appealing since they can simultaneously learn effective
image representations and compact hash codes. This paper focuses on
deep-networks-based hashing for multi-label images, each of which may contain
objects of multiple categories. In most existing hashing methods, each image is
represented by one piece of hash code, which is referred to as semantic
hashing. This setting may be suboptimal for multi-label image retrieval. To
solve this problem, we propose a deep architecture that learns
\textbf{instance-aware} image representations for multi-label image data, which
are organized in multiple groups, with each group containing the features for
one category. The instance-aware representations not only bring advantages to
semantic hashing, but also can be used in category-aware hashing, in which an
image is represented by multiple pieces of hash codes and each piece of code
corresponds to a category. Extensive evaluations conducted on several benchmark
datasets demonstrate that, for both semantic hashing and category-aware
hashing, the proposed method shows substantial improvement over the
state-of-the-art supervised and unsupervised hashing methods.Comment: has been accepted as a regular paper in the IEEE Transactions on
Image Processing, 201
Deep Multi-Index Hashing for Person Re-Identification
Traditional person re-identification (ReID) methods typically represent
person images as real-valued features, which makes ReID inefficient when the
gallery set is extremely large. Recently, some hashing methods have been
proposed to make ReID more efficient. However, these hashing methods will
deteriorate the accuracy in general, and the efficiency of them is still not
high enough. In this paper, we propose a novel hashing method, called deep
multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID.
DMIH seamlessly integrates multi-index hashing and multi-branch based networks
into the same framework. Furthermore, a novel block-wise multi-index hashing
table construction approach and a search-aware multi-index (SAMI) loss are
proposed in DMIH to improve the search efficiency. Experiments on three widely
used datasets show that DMIH can outperform other state-of-the-art baselines,
including both hashing methods and real-valued methods, in terms of both
efficiency and accuracy.Comment: 10 pages, 6 figures, 2 table
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity search in
many large-scale information retrieval systems, in particular, for text data.
Variational auto-encoders (VAEs) with binary latent variables as hashing codes
provide state-of-the-art performance in terms of precision for document
retrieval. We propose a pairwise loss function with discrete latent VAE to
reward within-class similarity and between-class dissimilarity for supervised
hashing. Instead of solving the optimization relying on existing biased
gradient estimators, an unbiased low-variance gradient estimator is adopted to
optimize the hashing function by evaluating the non-differentiable loss
function over two correlated sets of binary hashing codes to control the
variance of gradient estimates. This new semantic hashing framework achieves
superior performance compared to the state-of-the-arts, as demonstrated by our
comprehensive experiments.Comment: To appear in UAI 202
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