4 research outputs found
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
Asymmetric Deep Semantic Quantization for Image Retrieval
Due to its fast retrieval and storage efficiency capabilities, hashing has
been widely used in nearest neighbor retrieval tasks. By using deep learning
based techniques, hashing can outperform non-learning based hashing technique
in many applications. However, we argue that the current deep learning based
hashing methods ignore some critical problems (e.g., the learned hash codes are
not discriminative due to the hashing methods being unable to discover rich
semantic information and the training strategy having difficulty optimizing the
discrete binary codes). In this paper, we propose a novel image hashing method,
termed as \textbf{\underline{A}}symmetric \textbf{\underline{D}}eep
\textbf{\underline{S}}emantic \textbf{\underline{Q}}uantization
(\textbf{ADSQ}). \textbf{ADSQ} is implemented using three stream frameworks,
which consist of one \emph{LabelNet} and two \emph{ImgNets}. The
\emph{LabelNet} leverages the power of three fully-connected layers, which are
used to capture rich semantic information between image pairs. For the two
\emph{ImgNets}, they each adopt the same convolutional neural network
structure, but with different weights (i.e., asymmetric convolutional neural
networks). The two \emph{ImgNets} are used to generate discriminative compact
hash codes. Specifically, the function of the \emph{LabelNet} is to capture
rich semantic information that is used to guide the two \emph{ImgNets} in
minimizing the gap between the real-continuous features and the discrete binary
codes. Furthermore, \textbf{ADSQ} can utilize the most critical semantic
information to guide the feature learning process and consider the consistency
of the common semantic space and Hamming space. Experimental results on three
benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the
proposed \textbf{ADSQ} can outperforms current state-of-the-art methods.Comment: Accepted to IEEE ACCESS. arXiv admin note: text overlap with
arXiv:1812.0140
Deep Semantic Multimodal Hashing Network for Scalable Multimedia Retrieval
Hashing has been widely applied to multimodal retrieval on large-scale
multimedia data due to its efficiency in computation and storage. Particularly,
deep hashing has received unprecedented research attention in recent years,
owing to its perfect retrieval performance. However, most of existing deep
hashing methods learn binary hash codes by preserving the similarity
relationship while without exploiting the semantic labels of data points, which
result in suboptimal binary codes. In this work, we propose a novel Deep
Semantic Multimodal Hashing Network for scalable multimodal retrieval. In
DSMHN, two sets of modality-specific hash functions are jointly learned by
explicitly preserving both the inter-modality similarities and the
intra-modality semantic labels. Specifically, with the assumption that the
learned hash codes should be optimal for task-specific classification, two
stream networks are jointly trained to learn the hash functions by embedding
the semantic labels on the resultant hash codes. Different from previous deep
hashing methods, which are tied to some particular forms of loss functions, the
proposed deep hashing framework can be flexibly integrated with different types
of loss functions. In addition, the bit balance property is investigated to
generate binary codes with each bit having 50% probability to be 1 or -1.
Moreover, a unified deep multimodal hashing framework is proposed to learn
compact and high-quality hash codes by exploiting the feature representation
learning, inter-modality similarity preserving learning, semantic label
preserving learning and hash functions learning with bit balanced constraint
simultaneously. We conduct extensive experiments for both unimodal and
cross-modal retrieval tasks on three widely-used multimodal retrieval datasets.
The experimental result demonstrates that DSMHN significantly outperforms
state-of-the-art methods.Comment: 13 page
A Survey on Deep Hashing Methods
Nearest neighbor search is to find the data points in the database such that
the distances from them to the query are the smallest, which is a fundamental
problem in various domains, such as computer vision, recommendation systems and
machine learning. Hashing is one of the most widely used methods for its
computational and storage efficiency. With the development of deep learning,
deep hashing methods show more advantages than traditional methods. In this
paper, we present a comprehensive survey of the deep hashing algorithms.
Specifically, we categorize deep supervised hashing methods into pairwise
similarity preserving, multiwise similarity preserving, implicit similarity
preserving, classification-oriented preserving as well as quantization
according to the manners of preserving the similarities. In addition, we also
introduce some other topics such as deep unsupervised hashing and multi-modal
deep hashing methods. Meanwhile, we also present some commonly used public
datasets and the scheme to measure the performance of deep hashing algorithms.
Finally, we discussed some potential research directions in conclusion.Comment: 20 pages, 1 figur