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.

本文介绍了一种基于深度神经网络和实例感知图像表示的图像哈希方法，旨在解决多标签图像检索中单哈希码表示的不足，并在多个基准数据集上进行了广泛评估，证明了该方法可以显著提高语义哈希和类别感知哈希的检索性能。

多标签图像检索的实例感知哈希

Instance-Aware Hashing for Multi-Label Image Retrieval

Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.

本文提出了一种深度学习结构的监督哈希方法，其中图像通过经过精心设计的卷积神经网络映射为二进制代码，通过三个构建模块的流水线过程（卷积子网络，划分编码模块和三元组排序损失），实现最近邻搜索的高效率和精确度。

深度神经网络中的同时特征学习与哈希编码

Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

In recent years, a lot of attention has been devoted to efficient nearest
neighbor search by means of similarity-preserving hashing. One of the plights
of existing hashing techniques is the intrinsic trade-off between performance
and computational complexity: while longer hash codes allow for lower false
positive rates, it is very difficult to increase the embedding dimensionality
without incurring in very high false negatives rates or prohibiting
computational costs. In this paper, we propose a way to overcome this
limitation by enforcing the hash codes to be sparse. Sparse high-dimensional
codes enjoy from the low false positive rates typical of long hashes, while
keeping the false negative rates similar to those of a shorter dense hashing
scheme with equal number of degrees of freedom. We use a tailored feed-forward
neural network for the hashing function. Extensive experimental evaluation
involving visual and multi-modal data shows the benefits of the proposed
method.

本研究提出使用 feed-forward 神经网络来实现稀疏高维哈希码，并且通过对视觉和多模态数据的实验评估表明该方法具有显著的优势。

稀疏相似性保持哈希

Sparse similarity-preserving hashing

Feature matching in omnidirectional vision systems is a challenging problem,
mainly because complicated optical systems make the theoretical modelling of
invariance and construction of invariant feature descriptors hard or even
impossible. In this paper, we propose learning invariant descriptors using a
training set of similar and dissimilar descriptor pairs. We use the
similarity-preserving hashing framework, in which we are trying to map the
descriptor data to the Hamming space preserving the descriptor similarity on
the training set. A neural network is used to solve the underlying optimization
problem. Our approach outperforms not only straightforward descriptor matching,
but also state-of-the-art similarity-preserving hashing methods.

本文提出使用相似和不相似描述符对的训练集来学习不变描述符，通过神经网络解决优化问题，使用相似性保持哈希框架将描述符数据映射到汉明空间以保留训练集上的相似性，该方法不仅优于简单描述符匹配，也优于最先进的相似性保持哈希方法。