一种基于流形学习的图像检索特征降维方法

"维度灾难"是基于内容的图像检索(CBIR,content-based image retrieval)技术需要重点解决的关键问题。局保投影(LPP,locality preserving projections)流形学习算法可以最大限度地保留图像的局部非线性结构,从而更能够保留图像的本质特征。利用LPP流形学习算法的特性,在CBIR框架下,提出了一种图像检索特征降维方法。实验结果表明,方法在保持与原始特征基本相当的检索性能情况下,特征比对的计算复杂度可以降低66.51%。     

一种基于局部保形映射(LPP)的相关反馈方法

目前图像检索通常采用高效的图像降维算法和适当的相关反馈技术来提高检索的效率。局部保形映射(LPP)算法是保留图像本质特征的一种有效的线性降维算法。本文在LPP算法的基础上引入相关反馈技术,进一步提高了检索准确度。利用LPP算法得到降维子空间,在子空间上得出查询数据的k-近邻构成候选数据集,并与查询数据集构建一个权图G,通过弗洛伊德算法求得图G中任意两个数据点之间的测地线距离并排序进而得出反馈结果。实验表明,该算法提高了检索的准确度,并使检索结果得到一定的优化。     

基于Curvelet及LPP的人脸识别算法

基于人脸图像的曲线奇异性及高维图像数据带来的计算复杂性．提出一种结合Curvelet变换与LPP的人脸识别方法。首先通过Curvelet变换对人脸图像降维,利用LPP将图像投影到最优子空间中,利用支持向量机进行分类识别,实验结果表明该算法的识别效果优于小波变换结合LPP方法、LPP方法。     

保局投影在图像隐密检测中的应用

为解决图像隐密检测中图像特征维数过高导致的"维数灾难"问题,在保持图像特征内在低维结构的基础上降低特征向量的维数,方便构造更有效的分类器,提出了一种基于保局投影(locality preserving projections,LPP)降维的图像隐密检测算法,对待测图像进行小波变换形成图像特征后,利用LPP算法实现对图像高维特征的降维,得到图像特征集的低维流形.使用支持向量机(SVM)算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类.实验结果表明,与不使用降维算法的检测方案相比,基于LPP降维的检测算法能够显著地提高检测的准确率.     

超光谱遥感图像有监督LPP特征提取研究

在局部保留投影(LPP)特征提取算法的基础上,利用样本标签信息提出了一种有监督的局部保留投影算法(SPLPP),该算法的邻接图的权值不仅考虑了LPP算法中的相似性权值,而且加入了监督类的相关权值。SPLPP算法主要步骤是先用PCA去除高维超光谱遥感图像的冗余信息,再把监督机制引入到LPP中,实现图像的特征提取,将高维超光谱遥感图像投影到低维空间中,利于分类。应用SPLPP算法对高维的遥感原始超光谱图像进行特征提取后,利用支持向量机(SVM)和最近邻分类器(KNN)对降维后的遥感图像数据进行分类;并与PCA、LPP、LDA等特征提取算法进行了比较实验。实验表明:结合了LPP局部信息保留能力和全域标签信息的SPLPP算法,有更好的局部信息保留能力和类判别能力,使分类器分类精度更高,分类效果更好。     

应用视觉注意多分辨率分析的图像检索

基于人类视觉感知理论,提出一个改进的Itti视觉注意模型用于图像检索。该改进视觉注意模型是在充分考虑纹理特征与视觉感知关系的基础上,构造一个粗糙度图,用作视觉注意模型的一个初级视觉特征。首先通过该改进视觉注意模型得到50个视觉特征图;然后分别对每个视觉特征图采用局部二值模式傅里叶直方图（LBP-HF）方法抽取其分布信息,从而获得每幅图像的高维特征;最后利用局部保持投影(LPP)方法进行维数约简,以获取具有图像间局部几何和鉴别信息的低维特征用于图像检索。实验结果表明,该算法能获得较好的检索效果。     

基于子图像多特征组合的商标图像检索

本文提出了基于子图像特征组合的商标图像检索算法.首先对商标图像进行子图像抽取,然后根据子图像单特征计算图像与目标图像的单特征距离,最后基于多特征组合得到图像相似性度量.用Hu不变矩对基于子图像多特征组合的商标图像检索算法进行实验,用PVR指数作为图像检索性能评价准则.实验表明,相对基于全局图像单特征的检索算法,基于子图像多特征组合的商标图像检索算法具有更出色的检索性能,其检索结果更符合人眼的视觉感受.     

LPP算法和DLPP算法在掌纹识别中的应用研究

本文对近年来提出的局部保留映射（LPP）算法和判别局部保留映射（DLPP）算法思想进行了详细介绍,设计并完成了基于LPP和DLPP算法在掌纹识别中识别结果的对比实验。实验结果对基于这两种算法的掌纹识别方法给予数据支持,而且说明DLLP算法要优于LPP算法。     

基于HSV颜色特征图像检索算法在Matlab中的实现

在分析基于颜色特征的图像检索算法基础上,并实现了基于HSV颜色特征图像检索算法,实验证明,该算法的有效性和实用性。     

基于BP网络的相关反馈在图像检索中的应用

在研究图像检索基本理论基础上,提出基于主色提取和主色集扩充的图像检索算法,并运用基于BP网络的相关反馈方法提高算法性能,通过开发图像检索系统并进行检索实验,验证了算法的检索性能及BP网络相关反馈算法的有效性.     

一种新的基于局部保持投影的高维数据聚类成员构造方法

研究在高维数据中如何产生聚类成员,并提出一种新的构造聚类成员的方法。为解决高维数据的维度对构造成员带来的影响,新的构造方法在构造聚类成员之前利用局部保持投影先对高维数据进行维度约减,然后在约减后的子空间中用随机投影结合K均值方法构造聚类成员。最后讨论了局部保持投影子空间维度的选取。实验表明,新方法得到的结果要明显优于已有的主分量分析结合下采样方法和简单的随机投影方法。     

The equivalence of two classical list scheduling algorithms for dependent typed tasks with release dates,due dates and precedence delays

We consider a finite set of unit time execution tasks with release dates, due dates and precedence delays. The machines are partitioned into k classes. Each task requires one machine from a fixed class to be executed. The problem is the existence of a feasible schedule. This general problem is known to be \(\mathcal {NP}\)-complete; many studies were devoted to the determination of polynomial time algorithms for some special subcases, most of them based on a particular list schedule. The Garey–Johnson and Leung–Palem–Pnueli algorithms (respectively GJ and LPP in short) are both improving the due dates to build a priority list. They are modifying them using necessary conditions until a fixed point is reached. The present paper shows that these two algorithms are different implementations of the same generic one. The main consequence is that all the results valid for GJ algorithm are also for LPP and vice versa.     

Image retrieval based on incremental subspace learning

Many problems in information processing involve some form of dimensionality reduction, such as face recognition, image/text retrieval, data visualization, etc. The typical linear dimensionality reduction algorithms include principal component analysis (PCA), random projection, locality-preserving projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In this paper, we propose a semi-supervised subspace learning algorithm for image retrieval. In relevance feedback-driven image retrieval system, the user-provided information can be used to better describe the intrinsic semantic relationships between images. Our algorithm is fundamentally based on LPP which can incorporate user's relevance feedbacks. As the user's feedbacks are accumulated, we can ultimately obtain a semantic subspace in which different semantic classes can be best separated and the retrieval performance can be enhanced. We compared our proposed algorithm to PCA and the standard LPP. Experimental results on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.     

完备鉴别保局投影人脸识别算法

为了充分利用保局总体散布主元空间内的鉴别信息进行人脸识别,提出了一种完备鉴别保局投影(complete discriminant locality preserving projections,简称CDLPP)人脸识别算法.鉴于Fisher鉴别分析和保局投影已经被广泛的应用于人脸识别,完备鉴别保局投影(locality preserving projections,简称LPP)算法将这两者结合起来,分析了保局类内散布、类间散布和总体散布的主元空间和零空间内包含的鉴别信息.该算法采用奇异值分解(singular value decomposition,简称SVD),去除了不含任何鉴别信息的保局总体散布的零空间;分别在保局类内散布的主元空间和零空间提取规则鉴别特征和不规则鉴别特征;用串联的方式在特征层融合规则鉴别特征和不规则鉴别特征形成完备的鉴别特征进行人脸识别.在ORL库、FERET子库和PIE子库上的大量识别实验充分表明了完备鉴别保局投影算法的性能优于线性鉴别分析、保局投影和鉴别保局投影等现有的子空间人脸识别算法,验证了算法的有 效性.     

基于判别正则化的局部保留投影

局部保留投影（Locality preserving projections,LPP）是一种常用的线性化流形学习方法,其通过线性嵌入来保留基于图所描述的流形数据本质结构特征,因此LPP对图的依赖性强,且在嵌入过程中缺少对图描述的进一步分析和挖掘。当图对数据本质结构特征描述不恰当时,LPP在嵌入过程中不易实现流形数据本质结构的有效提取。为了解决这个问题,本文在给定流形数据图描述的条件下,通过引入局部相似度阈值进行局部判别分析,并据此建立判别正则化局部保留投影（简称DRLPP）。该方法能够在现有图描述的条件下,有效突出不同流形结构在线性嵌入空间中的可分性。在人造合成数据集和实际标准数据集上对DRLPP以及相关算法进行对比实验,实验结果证明了DRLPP的有效性。     

Supervised optimal locality preserving projection

In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Unlike the unsupervised learning scheme of LPP, this paper follows the supervised learning scheme, i.e. it uses both local information and class information to model the similarity of the data. Based on novel similarity, we propose two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP). Optimal here means that the extracted features via SOLPP (or NL-SOLPP) are statistically uncorrelated and orthogonal. We compare the proposed SOLPP and NL-SOLPP with LPP, orthogonal locality preserving projection (OLPP) and uncorrelated locality preserving projection (ULPP) on publicly available data sets. Experimental results show that the proposed SOLPP and NL-SOLPP achieve much higher recognition accuracy.     

Discover latent discriminant information for dimensionality reduction: Non-negative Sparseness Preserving Embedding

How to define sparse affinity weight matrices is still an open problem in existing manifold learning algorithms. In this paper, we propose a novel unsupervised learning method called Non-negative Sparseness Preserving Embedding (NSPE) for linear dimensionality reduction. Differing from the manifold learning-based subspace learning methods such as Locality Preserving Projections (LPP), Neighbor Preserving Embedding (NPE) and the recently proposed sparse representation based Sparsity Preserving Projections (SPP); NSPE preserves the non-negative sparse reconstruction relationships in low-dimensional subspace. Another novelty of NSPE is the sparseness constraint, which is directly added to control the non-negative sparse representation coefficients. This gives a more ground truth model to imitate the actions of the active neuron cells of V1 of the primate visual cortex on information processing. Although labels are not used in the training steps, the non-negative sparse representation can still discover the latent discriminant information and thus provides better measure coefficients and significant discriminant abilities for feature extraction. Moreover, NSPE is more efficient than the recently proposed sparse representation based SPP algorithm. Comprehensive comparison and extensive experiments show that NSPE has the competitive performance against the unsupervised learning algorithms such as classical PCA and the state-of-the-art techniques: LPP, NPE and SPP.     

改进的2DPCA人脸识别算法

在对2DPCA人脸识别方法研究的基础上,提出一种改进的2DPCA人脸识别算法,该算法对训练集进行两次2DPCA特征提取,以此重建散布矩阵,从而大大降低特征矩阵的存储空间.并在标准Yale与ORL人脸识别数据库上进行对比实验,改进的2DPCA人脸算法能有效改善识别性能,优于传统的2DPCA方法.最后,再通过和PCA,LD...