基于最近邻搜索耦合近邻损耗聚类的图像伪造检测算法

目的为了解决当前图像伪造检测算法在对图像进行伪造检测时,主要依靠全局搜索的方式来完成特征点匹配,导致其检测效率较低,且在对复杂伪造图像进行检测时,易出现检测精度不高和检测错误的不足。方法提出基于最近邻搜索耦合近邻损耗聚类的图像伪造检测算法。首先引入积分图像的方法,对图像进行预处理,借助Hessian矩阵行列式来提取特征点。利用特征点构建圆形区域,通过求取圆形区域内Haar小波响应获取特征点的特征描述符。然后通过特征描述符建立KD树索引,利用最近邻搜索方法代替SURF中全局搜索的方法,对SURF进行改进,完成特征点的匹配。最后,利用特征点间的近邻关系求取近邻函数值,通过近邻函数值对特征点进行聚类,完成图像的伪造检测。结果实验结果显示,与当前图像伪造检测算法相比,所提算法具有更高的检测效率以及更高的检测正确度。结论所提算法具备较高的检测精度,在印刷防伪与信息安全等领域具有较好的应用价值。     

一种基于形状分析的R树节点分裂算法

基于对最小边界矩形(MBR)形状的分析,提出了一种线性时间复杂度的R树空间索引节点分裂算法。该算法将节点及其记录的最小边界矩形按形状分类,并根据分类情况确定节点分裂策略。首先提出了一种基于形状分析的基本节点分裂算法,然后针对其可能产生的不平衡分裂结果,提出了一种分裂结果平衡算法。最后提出了一种考虑兄弟节点的联合分裂策略以进一步提升算法的效果。对比实验表明,无论在索引的创建过程还是查询过程,此算法效率都优于对比算法,并且具有易实现和适应性强等特点,可以应用于各种空间数据库和地理信息系统(GIS)。     

一种高效的GNN查询算法的研究

给定集合P和Q,则GNN查询返回的是P中的对象p,p满足到Q中所有对象的距离的集合函数f最小。求GNN的方法有多种,文章提出了vp-GNN算法,其特点在于不采用任何索引的情况下,也能高效地对空间数据库中的数据进行裁剪,缩小查询区域、提高GNN的查询效率。     

高斯型点扩展函数估计的最近邻算法

本文针对计算光学切片中的最近邻算法提出了一种改进算法.通过小波变换计算出高斯点扩展函数的方差值,再根据相邻图像成像及高斯函数特性,得出所需的高斯型层间点扩展函数.同时,文章还给出了两种高斯型层间点扩展函数方差的获得方式及获得过程,对最近邻算法中的加权因子的取值范围做出了讨论,对传统的最近邻算法做出了改进.实验表明,本算法能够更有效地复原符合最近邻要求的切片图像.在点扩展函数未知的情况下,复原效果要优于传统方法.     

DDIS：一种新的支持快速相似检索的高维索引结构

基于内容的图像检索是一种典型的相似检索问题,在海量高维的数据集中进行相似匹配,一般认为距离计算费用很高。因此,需要建立有效的索引结构,以减少每个查询中的距离计算次数。为此,针对K最近邻查询（KNN）算法．提出了一种新的高维空间数据索引方法——层次距离索引（DepthDistanceIn—dexStructure,DDIS）。详细讨论了这种索引结构的建立与检索等问题,并给出了相应的算法,实验结果显示了这种索引技术的有效性。     

基于B~Z树深度优先高维空间范围查询算法

考虑到在低维空间中基于线性扫描、R树、VA文件和NB树的空间范围查询算法的查询效率较高,而在高维空间中这些算法均出现不同程度的性能恶化现象,将降低空间维度作为解决高维空间范围查询问题的关键,并利用基于Z曲线的网格划分方法降低空间维度,使用Z区域聚类相似数据给出了一种改进的索引结构B~Z树,提出了一种深度优先高维空间范围查询算法ZRRQ。该算法采用高效剪枝策略,能够快速遍历B~Z树。实验结果表明,在高维空间中该算法优于基于线性扫描、R树、VA文件和NB树的空间范围查询算法。     

基于局部特征尺度分解和核最近邻凸包分类算法的滚动轴承故障诊断方法

提出了一种基于局部特征尺度分解(Local characteristic-scale decomposition,LCD)和核最近邻凸包(Kernelnearest neighbor convex hull,KNNCH)分类算法的滚动轴承故障诊断方法。采用LCD方法对滚动轴承原始振动信号进行分解得到若干内禀尺度分量(Intrinsic scale component,ISC),然后将这些ISC分量组成初始特征向量矩阵,再对该矩阵进行奇异值分解,提取奇异值作为故障特征向量并输入到KNNCH分类器,根据其输出结果来判断滚动轴承的工作状态和故障类型。LCD方法是一种新的自适应时频分析方法,非常适用于非平稳信号的处理,而KNNCH算法是一种基于核函数方法,并将凸包估计与最近邻分类思想相融合的模式识别算法,可直接应用于多类问题且需优化的参数只有核参数。实验分析结果表明,所提出的方法能有效地提取滚动轴承故障特征信息,而且在小样本的情况下仍能准确地对滚动轴承的工作状态和故障类型进行分类。同时,与支持向量机(Support vec-tor machine,SVM)算法的对比分析结果表明,KNNCH算法的分类性能的稳定性要高于SVM算法。     

一种基于SQL变换的分布式空间查询优化算法

基于新一代GIS技术体系的空间数据的分布特点,提出了一种新的应用于分布式空间查询处理的混合启发式优化算法(HHOA).该算法参考了传统的基于关系代数变换的启发式优化算法,同时为了克服单调采用直接连接或者半连接的弊端,引入了直接连接和半连接相混合的策略,实现更高的查询执行效率.实例研究表明,尤其是在涉及空间连接的查询前提下,该算法能够有效支持分布式空间查询的处理.     

基于字典树的基数排序算法

排序和查找是数据处理中最常用的运算之一,比较排序算法中时间复杂度最小的是快速排序,接近O(nlog2n)。基数排序是非比较排序算法中的一种,设待排序列为n个记录,d个关键码,关键码的取值范围为radix,则进行链式基数排序的时间复杂度为O(d(n+radix))。字典树是常用于字符串排序的算法,时间复杂度为O(n),排序速度快而且便于查找。结合两种算法的优点,提出一种更高效且合理的算法。     

基于敏感特征选择与流形学习维数约简的故障诊断

针对故障诊断中特征集包含非敏感特征和维数过高的问题,提出基于特征选择(Feature selection, FS)与流形学习维数约简的故障诊断方法。提出了一种改进的核空间距离测度特征选择方法(Improved kernel distance measurement feature selection, IKMD-FS),在核空间中计算样本类间距离和类内散度,优选出使样本类间距大、类内散度小的特征,并根据特征的敏感程度对特征进行加权。通过线性局部切空间排列算法(Linear local tangent space alignment, LLTSA)对由敏感特征组成的特征子集进行特征融合,提取出对故障分类更加敏感的融合特征,并输入加权k最近邻分类器(Weighted k nearest neighbor classifier, WKNNC)进行故障识别。WKNNC具有比k最近邻分类器(k nearest neighbor classifier, KNNC)更加稳定的识别精度。最后,通过滚动轴承故障模拟实验验证了本文方法的有效性。     

Feature selection and weighting by nearest neighbor ensembles

In the field of statistical discrimination nearest neighbor methods are a well known, quite simple but successful nonparametric classification tool. If the number of predictors increases, however, predictive power normally deteriorates. In general, if some covariates are assumed to be noise variables, variable selection is a promising approach. The paper's main focus is on the development and evaluation of a nearest neighbor ensemble with implicit variable selection. In contrast to other nearest neighbor approaches we are not primarily interested in classification, but in estimating the (posterior) class probabilities. In simulation studies and for real world data the proposed nearest neighbor ensemble is compared to an extended forward/backward variable selection procedure for nearest neighbor classifiers, and some alternative well established classification tools (that offer probability estimates as well). Despite its simple structure, the proposed method's performance is quite good — especially if relevant covariates can be separated from noise variables. Another advantage of the presented ensemble is the easy identification of interactions that are usually hard to detect. So not simply variable selection but rather some kind of feature selection is performed.     

Threshold error penalty for fault-tolerant quantum computation with nearest neighbor communication

The error threshold for fault-tolerant quantum computation with concatenated encoding of qubits is penalized by internal communication overhead. Many quantum computation proposals rely on nearest neighbor communication, which requires excess gate operations. For a qubit stripe with a width of L+1 physical qubits implementing L levels of concatenation, we find that the error threshold of 2.1/spl times/10/sup -5/ without any communication burden is reduced to 1.2/spl times/10/sup -7/ when gate errors are the dominant source of error. This /spl sim/175/spl times/ penalty in error threshold translates to an /spl sim/13/spl times/ penalty in the amplitude and timing of gate operation control pulses.     

An efficient nearest neighbor searching algorithm with application to LBG codebook generation

Abstract

The nearest neighbor (NN) searching problem has wide applications. In vector quantization (VQ), both the codebook generation phase and encoding phase (using the codebook just generated) often need to use the NN search. Improper design of the searching algorithm will make the complexity quite big as vector dimensionality k or codebook size N increases. In this paper, a fast NN searching method is proposed, which can then accelerate the LBG codebook generation process for VQ design. The method successfully modifies and improves the LAESA method. Unlike LAESA, the proposed k/2 “fixed” points (allocated far from the data) and the origin are used as the k/2+1 reference points to reduce the searching area. The overhead in memory is only linearly proportional to N and k. The time complexity, including the overhead, is of order O(kN). According to our experiments, the proposed algorithm can reduce the time burden while the distortion remains identical to that of the full search.     

A particle scale mixing measurement method using a generalized nearest neighbor mixing index

The dependence of mixing measurement on the sampling method and scale is a major concern in the characterization of the state of homogeneity of solid particle mixtures. A novel “particle neighborhood” based mixing index, called the generalized nearest neighbor (GNN) mixing index, is developed to measure solids mixing at the particle scale. GNN is a statistically robust, grid-independent index and provides reliable particle scale mixture homogeneity values. The GNN index has a further advantage that it can be readily adapted to mixtures of unequal proportions or mixtures containing more than two species. To test the GNN index, X-ray computed tomography (CT) images are obtained to noninvasively extract detailed particle distribution of binary mixtures consisting of different-density particles in the mixing vessel. CT images are acquired at different mixing stages to accurately describe mixture homogeneity evolution during the mixing process. Mixture homogeneity values are quantified using the novel GNN mixing index, and these values are compared with measurements obtained using different mixing indices, including a standard deviation-based mixing index and a grid-independent location-based mixing index. The GNN mixing index is found to be well-suited for reliable mixture homogeneity reporting at the particle scale.     

Direct and gradient-based average strain estimation by using weighted nearest neighbor cross-correlation peaks

In this paper, two novel approaches, gradientbased and direct strain estimation techniques, are proposed for high-quality average strain imaging incorporating a cost function maximization. Stiffness typically is a continuous function. Consequently, stiffness of proximal tissues is very close to that of the tissue corresponding to a given data window. Hence, a cost function is defined from exponentially weighted neighboring pre- and post-compression RF echo normalized cross-correlation peaks in the lateral (for displacement estimation) or in both the axial and the lateral (for direct strain estimation) directions. This enforces a controlled continuity in displacement/strain and average displacement/strain is calculated from the corresponding maximized cost function. Axial stress causes lateral shift in the tissue. Therefore, a 1-D post-compression echo segment is selected by incorporating Poisson's ratio. Two stretching factors are considered simultaneously in gradient-based strain estimation that allow imaging the lesions properly. The proposed time-domain gradient-based and direct-strain-estimation-based algorithms demonstrate significantly better performance in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), peak signal-to-noise ratio (PSNR), and mean structural similarity (MSSIM) than the other reported time-domain gradientbased and direct-strain-estimation techniques in finite element modeling (FEM) simulation and phantom experiments. For example, in FEM simulation, it has been found that the proposed direct strain estimation method can improve up to approximately 2.49 to 8.71, 2.2 to 6.63, 1.5 to 5, and 1.59 to 2.45 dB in the SNRe, CNRe, PSNR, and MSSIM compared with the traditional direct strain estimation method, respectively, and the proposed gradient-based algorithm demonstrates 2.99 to 16.26, 18.74 to 23.88, 3 to 9.5, and 0.6 to 5.36 dB improvement in the SNRe, CNRe, PSNR, and MSSIM, respectively, compared with a recently reported time-domain gradient-based technique. The range of improvement as noted above is for low to high applied strains. In addition, the comparative results using the in vivo breast data (including malignant or benign masses) also show that the lesion size is better defined by the proposed gradient-based average strain estimation technique.     

基于线性回归的相关查询推荐

在分析搜索引擎查询日志的基础上,提出了一种基于线性回归的相关查询推荐方法。该方法考虑了查询串之间的多种关联关系,包括查询串会话共现、点击页面共享和查询串文本相似性,以避免因直接应用查询串之间的部分关联关系造成易受查询日志中噪音的影响。相比以往的方法,采用线性回归的方法来识别相关查询推荐的有效特征,能更好地解决噪音问题和进行有效的相关查询推荐。实验证实,采用线性回归挖掘的识别中文相关查询串的特征非常有效,且所提出的回归模型的预测准确率优于现有的方法。     

Measurement of microchannel flow with digital holographic microscopy by integrated nearest neighbor and cross-correlation particle pairing

A micro digital in-line holographic particle tracking velocimetry (micro-DHPTV) system has been developed and applied to investigate the three-dimensional flow field in straight and Y-junction microchannels. The micro-DHPTV system comprises a cooled frame-transfer CCD camera and a double-pulsed laser. The processing algorithm introduced to evaluate the three-dimensional velocity is based on the combination of integrated cross-correlation and nearest neighbor matching algorithms, taking advantage of information from both the reconstructed particle field and the original holograms fringes patterns. Tests on simulated pairs of holograms show that the particles can be detected, located, and paired with high probability and accuracy. Results obtained in the straight and Y-junction microchannels show that the superimposed vector field is physically reasonable.     

Binary operator design by k‐nearest neighbor learning with application to image resolution increasing

In a typical office environment, heterogeneous devices and software, each working in a different spatial resolution, must interact. As a result resolution conversion problems arise frequently. This paper addresses the spatial resolution increasing of binary images and documents (e.g., conversion of a 300‐dots per inch [dpi] image into 600 dpi). A new, accurate and efficient solution to this problem is proposed. It makes use of the k‐nearest neighbor learning to design automatically a windowed zoom operator starting from pairs of in‐out sample images. The resulting operator is stored in a look‐up table, which is extremely fast computationally and therefore fit for real‐time applications. It is useful to know a priori the sample complexity (the quantity of training samples needed to get, with probability 1‐δ, an operator with accuracy ε). We use the probably approximately correct (PAC) learning theory to compute sample complexity, for both noise‐free and noisy cases. Because the PAC theory yields an overestimated sample complexity, the statistical estimation is used to estimate, a posteriori, a tight error bound. The statistical estimation is also used to show that the k‐nearest neighbor learning has a good inductive bias that allows reduction of the quantity of training sample images needed. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 331–339, 2000     

一种基于偏好的查询扩展方法

为克服语言歧义性和用户使用简短查询语句的习惯对搜索引擎查询准确率造成的影响,提出了一种基于偏好的查询扩展方法.该方法将用户对网页的偏好转化为对知识库中概念的偏好,建立用户兴趣模型,在该模型基础上对原始查询结果进行分析,挑选出与用户偏好关联最紧密的关键词加入原查询,解决了基于局部分析的查询扩展方法在初次检索质量不高时性能恶化的问题.实验表明,本文提出的方法相对于传统的查询扩展算法可以大幅度提高查询精度.