结合VLAD特征和稀疏表示的图像检索

为了实现快速准确的图像检索目标,提出一种结合VLAD（局部聚合描述符）特征和稀疏表示的图像检索方法。首先,根据图像具有结构细节丰富、局部视觉特征差异明显的特点,提取图像的局部旋转不变SURF特征,并采用局部聚合描述符方法,构造具有旋转不变性的图像VLAD特征,然后将VLAD特征与稀疏表示相结合,设计基于稀疏表示的相似性检索度量准则,实现图像的查询检索。实验结果表明,提出方法在查准率（precision）及平均归一化修正检索排序等指标上,均优于其他几种典型方法,并具有较高的计算效率。     

A unified image retrieval framework on local visual and semantic concept-based feature spaces

This paper presents a learning-based unified image retrieval framework to represent images in local visual and semantic concept-based feature spaces. In this framework, a visual concept vocabulary (codebook) is automatically constructed by utilizing self-organizing map (SOM) and statistical models are built for local semantic concepts using probabilistic multi-class support vector machine (SVM). Based on these constructions, the images are represented in correlation and spatial relationship-enhanced concept feature spaces by exploiting the topology preserving local neighborhood structure of the codebook, local concept correlation statistics, and spatial relationships in individual encoded images. Finally, the features are unified by a dynamically weighted linear combination of similarity matching scheme based on the relevance feedback information. The feature weights are calculated by considering both the precision and the rank order information of the top retrieved relevant images of each representation, which adapts itself to individual searches to produce effective results. The experimental results on a photographic database of natural scenes and a bio-medical database of different imaging modalities and body parts demonstrate the effectiveness of the proposed framework.     

Texture-based image indexing in the process of lossless data compression

The authors describe a new approach for content-based image indexing and retrieval by extracting texture features from the process of image compression via JPEG-LS. Since the compression technique adopted incorporates local edge detection to formulate predictive values for pixels being encoded, the texture features extracted by the proposed algorithms are also capable of describing image content in terms of edges and shapes of local objects without adding any significant complexity to the original JPEG-LS. While lossless data compression helps in saving storage space automatically for image databases, the extensive experiments also show that this type of feature extraction produces better retrieval results in comparison with existing similar indexing techniques which are carried out without data compression.     

基于改进哈希算法的图像检索方法

当前主流图像检索技术所采用的传统视觉特征编码缺少足够的学习能力,影响学习得到的特征表达能力。此外,由于视觉特征维数高,会消耗大量的内存,因此降低了图像检索的性能。文中基于深度卷积神经网络与改进的哈希算法,提出并设计了一种端到端训练方式的图像检索方法。该方法将卷积神经网络提取的高层特征和哈希函数相结合,学习到具有足够表达能力的哈希特征,从而在低维汉明空间中完成对图像数据的大规模检索。在两个常用数据集上的实验结果表明,所提出的哈希图像检索方法的检索性能优于当前的一些主流方法。     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。     

Feature-specific vector quantization of images

A new interband vector quantization of a human vision-based image representation is presented. The feature specific vector quantizer (FVQ) is suited for data compression beyond second-order decorrelation. The scheme is derived from statistical investigations of natural images and the processing principles of biological vision systems, the initial stage of the coding algorithm is a hierarchical, and orientation-selective, analytic bandpass decomposition, realized by even- and odd-symmetric filter pairs that are modeled after the simple cells of the visual cortex. The outputs of each even- and odd-symmetric filter pair are interpreted as real and imaginary parts of an analytic bandpass signal, which is transformed into a local amplitude and a local phase component according to the operation of cortical complex cells. Feature-specific multidimensional vector quantization is realized by combining the amplitude/phase samples of all orientation filters of one resolution layer. The resulting vectors are suited for a classification of the local image features with respect to their intrinsic dimensionality, and enable the exploitation of higher order statistical dependencies between the subbands. This final step is closely related to the operation of cortical hypercomplex or end-stopped cells. The codebook design is based on statistical as well as psychophysical and neurophysiological considerations, and avoids the common shortcomings of perceptually implausible mathematical error criteria. The resulting perceptual quality of compressed images is superior to that obtained with standard vector quantizers of comparable complexity.     

MLDNet: Multi-level dense network for multi-focus image fusion

In this study, we propose a new deep learning architecture named Multi-Level Dense Network (MLDNet) for multi-focus image fusion (MFIF). We introduce shallow and dense feature extraction in our feature extraction module to extract images features in a more robust way. In particular, we extracted the features from a mixture of many distributions from prior to the complex distribution through densely connected convolutional layers, then the extracted features are fused to form dense local feature maps. We added global feature fusion into the proposed architecture in order to merge the dense local feature maps of each source image into a fused image representation for the reconstruction of the final fused image. Our proposed MLDNet learns feature extraction, feature fusion and reconstruction within the same network to provide an end-to-end solution for MFIF. Experimental results demonstrate that our proposed method achieved significant performance against different state-of-the-art MFIF methods.     

Context-aware features and robust image representations

Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation.     

单演信号在红外图像目标分类中的应用研究

红外成像是夜间观测的重要手段,在军事民用领域都有着广泛运用。针对红外图像目标分类问题,将单演信号引入用于特征提取,用于对目标特性的分析。经过单演信号处理后的红外图像可用幅度、相位和方位三个成分描述。对于每一个成分的多尺度结果,采用矢量串接以及降采样结合的方式构建单一特征矢量。最终构造得到的三个特征矢量能够反映目标的多层次特性。采用联合稀疏表示作为三种单演信号特征矢量的表征模型。在重构过程中,充分利用三类特征之间的关联性从而提高整体重构精度。在不同类别上按照联合稀疏表示的求解结果计算对于测试样本的重构误差,进而决定测试样本的类别信息。该方法通过单演信号获取红外图像中目标的多层次特性,基于联合稀疏表示模型对这些特征进行充分分析和挖掘,从而提高目标分类的精度和稳健性。实验基于公开的中波红外（Medium wave infrared,MWIR）图像数据集开展,分别对原始样本、模拟噪声样本以及模拟遮挡样本进行分类。根据实验结果,并与几类现有算法对比,反映了所提方法对于红外图像目标分类问题能够取得更高的有效性和稳健性。     

CMDC:an iterative algorithm for complementary multi-view document clustering

In response to the problems traditional multi-view document clustering methods separate the multi-view document representation from the clustering process and ignore the complementary characteristics of multi-view document clustering,an iterative algorithm for complementary multi-view document clustering——CMDC was proposed,in which the multi-view document clustering process and the multi-view feature adjustment were conducted in a mutually unified manner.In CMDC algorithm,complementary text documents were selected from the clustering results to aid adjusting the contribution of view features via learning a local measurement metric of each document view.The complementary text document of the results among the dimensionality clusters was selected by CMDC,and used to promote the feature tuning of the clusters.The partition consistency of the multi-dimensional document clustering was solved by the measure consistency of the dimensions.Experimental results show that CMDC effectively improves multi-dimensional clustering performance.     

基于多示例学习的图像检索方法

多示例学习对处理各类歧义问题有较好的效果,将它应用于周像检索问题,提出了一种新的基于多示例学习的图像检索方法。首先提取每幅图像的局部区域特征,通过对这些特征聚类求得一组基向量,并利用它们对每个局部特征向量进行编码,接着使用均值漂移聚类算法对图像进行分割,根据局部特征点位置所对应的分割块划分特征编码到相应的子集,最后将每组编码子集聚合成一个向量,这样每幅图像对应一个多示例包。根据用户选择的图像生成正包和反包,采用多示例学习算法进行学习,取得了较为满意的结果。     

图像分块匹配的SAR目标识别方法

提出基于分块匹配的合成孔径雷达（Synthetic Aperture Radar,SAR）目标识别方法。对待识别SAR图像进行4分块处理,每个分块描述目标的局部区域。对于每个分块,基于单演信号构造特征矢量,描述其时频分布以及局部细节信息。单演信号从幅度、相位以及局部方位3个层次对图像进行分解,可有效描述图像的局部变化情况,对于扩展操作条件下的目标变化分析具有重要的参考意义。对于构造得到的4个特征矢量,分别采用稀疏表示分类（Sparse Representation-based Classification,SRC）分别进行分类,获得相应的重构误差矢量。在此基础上,按照线性加权融合的基本思想,通过构造随机权值矩阵进行分析。对于不同权值矢量下获得的结果,经统计分析构造有效的决策变量,通过比较不同训练类别的结果,判定测试样本的类别。所提方法在特征提取和分类决策过程中充分考虑SAR图像获取条件的不确定,通过统计分析获得最优决策结果。实验在MSTAR数据集上设置和开展,包含了1类标准操作条件和3类扩展操作条件。通过与现有几类方法对比,有效证明了所提方法的有效性。     

基于显著性区域和蚁群算法的图像检索研究

针对提取到的图像特征受背景信息干扰,不能有针对性地提取到所需要的图像信息影 响检索精度。为了解决这一问题,本文提出一种基于改进VGGNet(visual geometry group network)和蚁群算法的图像显著性区 域检索算法。首先,利用类激活映射(class activation mapping, CMA)算法对图像显著性区域进行提取,剔除图像背景信息;然后使 用训练好的RS-VGG16模型提取图像显著性区域特征来表征图像。引入主成分分析(principal component analysis, PCA)算法,对高维特征 进行降维的同时减少特征信息的损失。最后,引入蚁群算法对检索结果进行优化。在corel_ 5000数据集上,选取基于VGG16网络的图像全局特征检索算法以及传统的BOF (bag of features)图像检索算法进 行对比试验。本文提出算法相较于基于VGG16网络的图像检索算法,平均查准率(mean average precision, MAP)值平均提升约4.36% ,相较于传统的BOF算法,MAP值平均提升约16.99%。实验结果表明本 文提出算法能够很好地去除图像背景信息的干扰,具有更优的检索性能。     

Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning

Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of content-based image retrieval (CBIR). In this paper, we propose a new feedback approach with progressive learning capability combined with a novel method for the feature subspace extraction. The proposed approach is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. Positive examples are used to estimate a Gaussian distribution that represents the desired images for a given query; while the negative examples are used to modify the ranking of the retrieved candidates. In addition, feature subspace is extracted and updated during the feedback process using a principal component analysis (PCA) technique and based on user's feedback. That is, in addition to reducing the dimensionality of feature spaces, a proper subspace for each type of features is obtained in the feedback process to further improve the retrieval accuracy. Experiments demonstrate that the proposed method increases the retrieval speed, reduces the required memory and improves the retrieval accuracy significantly.     

Beyond visual word ambiguity: Weighted local feature encoding with governing region

Typically, k-means clustering or sparse coding is used for codebook generation in the bag-of-visual words (BoW) model. Local features are then encoded by calculating their similarities with visual words. However, some useful information is lost during this process. To make use of this information, in this paper, we propose a novel image representation method by going one step beyond visual word ambiguity and consider the governing regions of visual words. For each visual application, the weights of local features are determined by the corresponding visual application classifiers. Each weighted local feature is then encoded not only by considering its similarities with visual words, but also by visual words’ governing regions. Besides, locality constraint is also imposed for efficient encoding. A weighted feature sign search algorithm is proposed to solve the problem. We conduct image classification experiments on several public datasets to demonstrate the effectiveness of the proposed method.     

基于全局和局部颜色特征的图像检索方法

提出了一种基于图像全局和局部颜色特征的图像检索方法.首先在符合视觉感知特性的Lab颜色空间中提取全局颜色特征;再对图像进行图像子块划分,同时利用具有人眼视觉特性的高斯加权系数对其进行加权,然后利用二值化得到的颜色位图作为局部颜色特征,并进一步加入了方向性的考虑,对图像子块进行垂直和水平投影,最后合理地融合了全局和局部颜色特征的相似性进行图像检索.对Corel图像数据库的实验结果表明,此算法具有良好的检索效率.     

BGT: A blind image quality evaluator via gradient and texture statistical features

Blind image quality assessment (BIQA) aims to design a model that can accurately evaluate the quality of the distorted image without any information about its reference image. Previous studies have shown that gradients and textures of image is widely used in image quality evaluation tasks. However, few studies used the joint statistics of gradient and texture information to evaluate image quality. Considering the visual perception characteristics of the human visual system, we develop a novel general-purpose BIQA model via two sets of complementary perception features. Specifically, the joint statistical histograms of gradient and texture are extracted as the first set of features, and the second set of features is extracted using the local binary pattern (LBP) operator. After extracting two groups of complementary quality-aware features, the feature vectors are sent to the support vector regression machine to establish the nonlinear relationship between quality-aware features and quality scores. A large number of experiments on seven large benchmark databases show that the proposed BIQA model has higher accuracy, better generalization properties and lower computational complexity than the relevant state-of-the-art BIQA metrics.     

一种掌纹纹线结构特征的描述和匹配方法

掌纹的纹线结构特征明显,稳定且具有足够的辨别信息。由于掌纹线很不规则,很难用数学表达式的方式对其结构进行精确描述。该文提出一种新的用于掌纹识别的纹线结构特征描述和匹配方法。该方法应用改进的注水算法从二值的纹线图像上提取纹线的统计结构特征;标记纹线方向,提取纹线的方向结构特征;将掌纹线的全局统计结构特征和局部细节方向结构特征整合为一个特征向量描述掌纹纹线的结构特征;最后定义一个加权距离衡量不同纹线结构特征之间的相似度。实验表明,该方法不仅具有较好的匹配性能,而且可以获得较高的识别精度。