11,417 research outputs found
A Deep Hashing Learning Network
Hashing-based methods seek compact and efficient binary codes that preserve
the neighborhood structure in the original data space. For most existing
hashing methods, an image is first encoded as a vector of hand-crafted visual
feature, followed by a hash projection and quantization step to get the compact
binary vector. Most of the hand-crafted features just encode the low-level
information of the input, the feature may not preserve the semantic
similarities of images pairs. Meanwhile, the hashing function learning process
is independent with the feature representation, so the feature may not be
optimal for the hashing projection. In this paper, we propose a supervised
hashing method based on a well designed deep convolutional neural network,
which tries to learn hashing code and compact representations of data
simultaneously. The proposed model learn the binary codes by adding a compact
sigmoid layer before the loss layer. Experiments on several image data sets
show that the proposed model outperforms other state-of-the-art methods.Comment: 7 pages, 5 figure
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
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
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
Hashing methods have been widely used for efficient similarity retrieval on
large scale image database. Traditional hashing methods learn hash functions to
generate binary codes from hand-crafted features, which achieve limited
accuracy since the hand-crafted features cannot optimally represent the image
content and preserve the semantic similarity. Recently, several deep hashing
methods have shown better performance because the deep architectures generate
more discriminative feature representations. However, these deep hashing
methods are mainly designed for supervised scenarios, which only exploit the
semantic similarity information, but ignore the underlying data structures. In
this paper, we propose the semi-supervised deep hashing (SSDH) approach, to
perform more effective hash function learning by simultaneously preserving
semantic similarity and underlying data structures. The main contributions are
as follows: (1) We propose a semi-supervised loss to jointly minimize the
empirical error on labeled data, as well as the embedding error on both labeled
and unlabeled data, which can preserve the semantic similarity and capture the
meaningful neighbors on the underlying data structures for effective hashing.
(2) A semi-supervised deep hashing network is designed to extensively exploit
both labeled and unlabeled data, in which we propose an online graph
construction method to benefit from the evolving deep features during training
to better capture semantic neighbors. To the best of our knowledge, the
proposed deep network is the first deep hashing method that can perform hash
code learning and feature learning simultaneously in a semi-supervised fashion.
Experimental results on 5 widely-used datasets show that our proposed approach
outperforms the state-of-the-art hashing methods.Comment: 14 pages, accepted by IEEE Transactions on Circuits and Systems for
Video Technolog
Deep Reinforcement Learning for Image Hashing
Deep hashing methods have received much attention recently, which achieve
promising results by taking advantage of the strong representation power of
deep networks. However, most existing deep hashing methods learn a whole set of
hashing functions independently, while ignore the correlations between
different hashing functions that can promote the retrieval accuracy greatly.
Inspired by the sequential decision ability of deep reinforcement learning, we
propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Our proposed DRLIH approach models the hashing learning problem as a sequential
decision process, which learns each hashing function by correcting the errors
imposed by previous ones and promotes retrieval accuracy. To the best of our
knowledge, this is the first work to address hashing problem from deep
reinforcement learning perspective. The main contributions of our proposed
DRLIH approach can be summarized as follows: (1) We propose a deep
reinforcement learning hashing network. In the proposed network, we utilize
recurrent neural network (RNN) as agents to model the hashing functions, which
take actions of projecting images into binary codes sequentially, so that the
current hashing function learning can take previous hashing functions' error
into account. (2) We propose a sequential learning strategy based on proposed
DRLIH. We define the state as a tuple of internal features of RNN's hidden
layers and image features, which can reflect history decisions made by the
agents. We also propose an action group method to enhance the correlation of
hash functions in the same group. Experiments on three widely-used datasets
demonstrate the effectiveness of our proposed DRLIH approach.Comment: 12 pages, submitted to IEEE Transactions on Multimedi
Improved Search in Hamming Space using Deep Multi-Index Hashing
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. There has been considerable
research on generating efficient image representation via the
deep-network-based hashing methods. However, the issue of efficient searching
in the deep representation space remains largely unsolved. To this end, we
propose a simple yet efficient deep-network-based multi-index hashing method
for simultaneously learning the powerful image representation and the efficient
searching. To achieve these two goals, we introduce the multi-index hashing
(MIH) mechanism into the proposed deep architecture, which divides the binary
codes into multiple substrings. Due to the non-uniformly distributed codes will
result in inefficiency searching, we add the two balanced constraints at
feature-level and instance-level, respectively. Extensive evaluations on
several benchmark image retrieval datasets show that the learned balanced
binary codes bring dramatic speedups and achieve comparable performance over
the existing baselines
Deep Attention-guided Hashing
With the rapid growth of multimedia data (e.g., image, audio and video etc.)
on the web, learning-based hashing techniques such as Deep Supervised Hashing
(DSH) have proven to be very efficient for large-scale multimedia search. The
recent successes seen in Learning-based hashing methods are largely due to the
success of deep learning-based hashing methods. However, there are some
limitations to previous learning-based hashing methods (e.g., the learned hash
codes containing repetitive and highly correlated information). In this paper,
we propose a novel learning-based hashing method, named Deep Attention-guided
Hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea
is to use guided hash codes which are generated by the hashing network of the
first stream framework (called first hashing network) to guide the training of
the hashing network of the second stream framework (called second hashing
network). Specifically, in the first network, it leverages an attention network
and hashing network to generate the attention-guided hash codes from the
original images. The loss function we propose contains two components: the
semantic loss and the attention loss. The attention loss is used to punish the
attention network to obtain the salient region from pairs of images; in the
second network, these attention-guided hash codes are used to guide the
training of the second hashing network (i.e., these codes are treated as
supervised labels to train the second network). By doing this, DAgH can make
full use of the most critical information contained in images to guide the
second hashing network in order to learn efficient hash codes in a true
end-to-end fashion. Results from our experiments demonstrate that DAgH can
generate high quality hash codes and it outperforms current state-of-the-art
methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.Comment: Accepted to IEEE ACCES
Deep Ordinal Hashing with Spatial Attention
Hashing has attracted increasing research attentions in recent years due to
its high efficiency of computation and storage in image retrieval. Recent works
have demonstrated the superiority of simultaneous feature representations and
hash functions learning with deep neural networks. However, most existing deep
hashing methods directly learn the hash functions by encoding the global
semantic information, while ignoring the local spatial information of images.
The loss of local spatial structure makes the performance bottleneck of hash
functions, therefore limiting its application for accurate similarity
retrieval. In this work, we propose a novel Deep Ordinal Hashing (DOH) method,
which learns ordinal representations by leveraging the ranking structure of
feature space from both local and global views. In particular, to effectively
build the ranking structure, we propose to learn the rank correlation space by
exploiting the local spatial information from Fully Convolutional Network (FCN)
and the global semantic information from the Convolutional Neural Network (CNN)
simultaneously. More specifically, an effective spatial attention model is
designed to capture the local spatial information by selectively learning
well-specified locations closely related to target objects. In such hashing
framework,the local spatial and global semantic nature of images are captured
in an end-to-end ranking-to-hashing manner. Experimental results conducted on
three widely-used datasets demonstrate that the proposed DOH method
significantly outperforms the state-of-the-art hashing methods
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
Deep supervised hashing has emerged as an influential solution to large-scale
semantic image retrieval problems in computer vision. In the light of recent
progress, convolutional neural network based hashing methods typically seek
pair-wise or triplet labels to conduct the similarity preserving learning.
However, complex semantic concepts of visual contents are hard to capture by
similar/dissimilar labels, which limits the retrieval performance. Generally,
pair-wise or triplet losses not only suffer from expensive training costs but
also lack in extracting sufficient semantic information. In this regard, we
propose a novel deep supervised hashing model to learn more compact class-level
similarity preserving binary codes. Our deep learning based model is motivated
by deep metric learning that directly takes semantic labels as supervised
information in training and generates corresponding discriminant hashing code.
Specifically, a novel cubic constraint loss function based on Gaussian
distribution is proposed, which preserves semantic variations while penalizes
the overlap part of different classes in the embedding space. To address the
discrete optimization problem introduced by binary codes, a two-step
optimization strategy is proposed to provide efficient training and avoid the
problem of gradient vanishing. Extensive experiments on four large-scale
benchmark databases show that our model can achieve the state-of-the-art
retrieval performance. Moreover, when training samples are limited, our method
surpasses other supervised deep hashing methods with non-negligible margins
Discrete Hashing with Deep Neural Network
This paper addresses the problem of learning binary hash codes for large
scale image search by proposing a novel hashing method based on deep neural
network. The advantage of our deep model over previous deep model used in
hashing is that our model contains necessary criteria for producing good codes
such as similarity preserving, balance and independence. Another advantage of
our method is that instead of relaxing the binary constraint of codes during
the learning process as most previous works, in this paper, by introducing the
auxiliary variable, we reformulate the optimization into two sub-optimization
steps allowing us to efficiently solve binary constraints without any
relaxation.
The proposed method is also extended to the supervised hashing by leveraging
the label information such that the learned binary codes preserve the pairwise
label of inputs.
The experimental results on three benchmark datasets show the proposed
methods outperform state-of-the-art hashing methods
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