Content-based image retrieval (CBIR) in large medical image archives is a
challenging and necessary task. Generally, different feature extraction methods
are used to assign expressive and invariant features to each image such that
the search for similar images comes down to feature classification and/or
matching. The present work introduces a new image retrieval method for medical
applications that employs a convolutional neural network (CNN) with recently
introduced Radon barcodes. We combine neural codes for global classification
with Radon barcodes for the final retrieval. We also examine image search based
on regions of interest (ROI) matching after image retrieval. The IRMA dataset
with more than 14,000 x-rays images is used to evaluate the performance of our
method. Experimental results show that our approach is superior to many
published works.

本研究提出了一种采用卷积神经网络和 Radon 条形码的医学图像检索方法，结果表明其在医疗图像中检索方面的表现优于其他已发表的方法。

基于卷积神经网络和 Radon 变换的快速医学图像检索生成二进制标签

Generating Binary Tags for Fast Medical Image Retrieval Based on  Convolutional Nets and Radon Transform

Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.

本文介绍了基于内容和标签的图像检索方法中，标签赋值、细化、检索三个问题的处理方法，分类讨论了各种方法的实现及其信息利用方式，并提出了实验方案以便更准确地评估算法表现。

社交化语义差距缩小：图像标签分配、精化和检索的比较调研

Socializing the Semantic Gap: A Comparative Survey on Image Tag  Assignment, Refinement and Retrieval

Most content-based image retrieval systems consider either one single query,
or multiple queries that include the same object or represent the same semantic
information. In this paper we consider the content-based image retrieval
problem for multiple query images corresponding to different image semantics.
We propose a novel multiple-query information retrieval algorithm that combines
the Pareto front method (PFM) with efficient manifold ranking (EMR). We show
that our proposed algorithm outperforms state of the art multiple-query
retrieval algorithms on real-world image databases. We attribute this
performance improvement to concavity properties of the Pareto fronts, and prove
a theoretical result that characterizes the asymptotic concavity of the fronts.

本文提出用于不同图像语义的多个查询图像的基于内容的图像检索算法，其中将 Pareto 前沿方法与有效流形排名相结合，证明该算法优于现有多个查询结果检索算法，并将该性能提高归因于 Pareto 前沿的凹性特性。

多查询图像检索的 Pareto-depth

Pareto-depth for Multiple-query Image Retrieval

Content-based image retrieval (CBIR) has been one of the most important
research areas in computer vision. It is a widely used method for searching
images in huge databases. In this paper we present a CBIR system called
NOHIS-Search. The system is based on the indexing technique NOHIS-tree. The two
phases of the system are described and the performance of the system is
illustrated with the image database ImagEval. NOHIS-Search system was compared
to other two CBIR systems; the first that using PDDP indexing algorithm and the
second system is that using the sequential search. Results show that
NOHIS-Search system outperforms the two other systems.

本文介绍了一个基于 NOHIS-tree 算法的 CBIR 系统 NOHIS-Search，该系统在对比 PDDP 索引算法和序列搜索后性能表现更优。