基于图谱分解的人脸表情分析

文中探索用人脸的几何结构图谱方法嵌入到模式空间来分析人脸表情,以图的加权邻接矩阵主要特征向量来定义矩阵的特征模。计算谱特征向量-模间邻接矩阵。用两类模式向量在范数下的多维尺度变换方法(MDS)嵌入该向量到一个模式空间,用人脸特征点来表示人脸图,并在模式空间里描述该嵌入方法下的同一人脸的不同表情。     

基于图谱分解的人脸表情分析

文中探索用人脸的几何结构图谱方法嵌入到模式空间米分析人脸表情，以网的加权邻接矩阵主要特征向量来定望矩阵的特征模。计算潜特征向量-模间邻接矩阵。用两类模式向量在范数下的多维尺度变换方法（MDS）嵌入该向量到一个模式空间，用人脸特征点来表示人脸图，并存模式空间里描述该嵌入方法下的同一人脸的不同表情。     

结构图的谱分解及聚类研究

探索用图谱方法嵌入和聚类非加权图,以图的邻接矩阵主要特征向量来定义邻接矩阵的特征模.对每个特征模,我们计算谱特征向量,包括特征模周界、特征模体积、Cheeger常数、模间邻接矩阵和模间边界距离.用两种对比方法嵌入这些向量到一个模式空间:1)用谱模式特征的协方差矩阵的主成分分析(PCA)和独立分量分析(ICA);2)两类模式向量在L2范数下的多维尺度变换方法(MDS).另外,我们在三维多面体的二维图像中用角点特征来表示邻近图,以描述不同嵌入方法的聚类效果.     

基于Gabor小波的人脸检测

提出了一种新的正面人脸检测算法。该方法组合了Gabor小波变换、输入图像的Gabor特征分析和Bayes分类器来进行正面人脸检测。对训练集的平均脸作Gabor小波变换得到40个投影向量；通过计算输入图像和这40个投影向量间的内积来提取图像的Gabor特征向量；训练Bayes分类器来进行正面人脸检测。实验结果表明，该算法的计算效率和检测精度均优于特征脸方法。     

基于图像似然度的人脸检测

本文提出了一种基于图像似然度的检测人脸方法,该方法能在复杂的背景下较好的检测出人脸。该方法分为训练阶段和检测阶段。在训练阶段。从人脸图像集中选取大量人脸图像的信息矩阵的奇异值向量作为矩阵中的一列而构成的人脸图像集特征矩阵。然后,用大量的人脸图像的特征向量与人脸图像集特征矩阵比较找出最小相似度,作为阈值;在检测阶段。求待测区域的特征向量与人脸特征矩阵的相似度,与阈值比较以决定是否是人脸。     

视频中适应光照可变情况下的人脸识别方法

提出一种结合使用自适应直方图均衡(AHE)、Gabor滤波器及局部三值模式(LTP)描述器进行视频中上下左右和正面光照条件下识别人脸的方法。首先,使用AHE对来自YaleB与CMU-PIE数据库的人脸图片进行降噪处理。然后用Gabor滤波器进行卷积,提取出相应的Gabor特征图,针对每一个Gabor特征图利用LTP描述器提取出局部邻域关系模式。最后由这些模式的区域直方图形成的序列来描述人脸。YaleB人脸库以及CMUPIE人脸库验证该方法的有效性。     

基于光流特征与高斯LDA的面部表情识别算法

文中提出了一种人脸表情识别的新方法,该方法采用动态的光流特征来描述人脸表情的变化差异,提高人脸表情的识别率。首先,计算人脸表情图像与中性表情图像之间的光流特征;然后,对传统的线性判断分析方法(Linear Discriminant Analysis,LDA)进行扩展,采用高斯LDA方法对光流特征进行映射,从而得到人脸表情图像的特征向量;最后,设计多类支持向量机分类器,实现人脸表情的分类与识别。在JAFFE和CK人脸表情数据库上的表情识别实验结果表明,该方法的平均识别率比3种对比方法的高出2%以上。     

基于关联图谱的视频片段聚类

提出一种基于关联图谱的视频片段聚类方法。构造视频片段的关联图并将其转换成邻接矩阵,提取邻接矩阵的主分量特征值、模间邻接矩阵和模间距离后,将三者分别嵌入主成分分析和独立成分分析模式空间中,利用k-means进行聚类分析。实验结果表明,该方法能有效区分不同类型的视频片段。     

标准正面人脸图像的识别

本论文选用人脸上27个特殊点作为人脸基本特征。以人脸几何结构为基础,结合有脸识别的心理特性,提出新颖、简便、高精度的“寻找存在”法,使提取特征点的速度、精度得到大大的提高,在详细分析这27个特列点的统计特性后,选择了其中信息量丰富的15个点间距及间距比构成一组向量代替人脸描述,用加权欧氏距离作为特征向量间相似性测试,在两类实验中,识别率高达100％和98％。     

视频结构化描述模型

如何有效地表示视频一直是多媒体领域的研究热点和难点。提出一种视频结构化描述模型,其基本思想是：利用视频的内蕴结构特点,以视频镜头作节点,以镜头间的相似度作边权,构建视频关联图模型,并提取视频关联图的谱特征,包括主分量特征、特征模容量、特征模周长、Cheeger常数、模间邻接矩阵、模间距离等。视频聚类和检索实验表明,视频结构化描述模型是可行的和有效的,其中主分量谱特征更表现了良好的性能。     

Spectral embedding of graphs

In this paper we explore how to embed symbolic relational graphs with unweighted edges in a pattern-space. We adopt a graph-spectral approach. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties. These include the eigenmode perimeter, eigenmode volume, Cheeger number, inter-mode adjacency matrices and intermode edge-distance. We embed these vectors in a pattern-space using two contrasting approaches. The first of these involves performing principal or independent components analysis on the covariance matrix for the spectral pattern vectors. The second approach involves performing multidimensional scaling on the L2 norm for pairs of pattern vectors. We illustrate the utility of the embedding methods on neighbourhood graphs representing the arrangement of corner features in 2D images of 3D polyhedral objects. Two problems are investigated. The first of these is the clustering of graphs representing distinct objects viewed from different directions. The second is the identification of characteristic views of single objects. These two studies reveal that both embedding methods result in well-structured view spaces for graph-data extracted from 2D views of 3D objects.     

A spectral approach to learning structural variations in graphs

This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis.     

一种基于结构分解和因子分析的贝叶斯网络隐变量发现算法

通过研究粗糙集与图论的关系,提出了以集合为权的加权多重完全多部图的概念,定义了加权多重完全多部图的邻接矩阵,得到了加权完全多部图与决策表的映射关系;给出了粗糙集决策表信息系统的图论形式和决策表信息系统属性约简的图论方法,并根据图论理论对算法进行了优化;得到了在决策表信息系统中,属性的集合不可以约简的充分必要条件;并进一步提出了基于属性置信度的计算方法和多决策属性的处理方法。编程实验结果证明该方法能有效地降低时间和空间复杂度。     

Graph edit distance from spectral seriation

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems.     

A block-based generative model for attributed network embedding

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.

     

基于双邻接图正交近邻保持投影的人脸识别算法

正交保持投影(ONPP)是经典的图嵌入降维技术,已经成功地应用到人脸识别中,其保持了高维数据的局部性和整体几何结构。监督的ONPP通过建立同类邻接图来最小化同类局部重构误差,寻找最优的低维嵌入,但是其只使用了类内信息,这会导致异类数据点间的结构不够明显。因此,提出了基于双邻接图的正交近邻保持投影(DAG-ONPP)算法。通过建立同类邻接图与异类邻接图,在数据嵌入低维空间后同类近邻重构误差尽量小,异类近邻重构误差更加明显。在ORL,Yale,YaleB和PIE人脸库上的实验结果表明,与其他经典算法相比,所提方法有效提高了分类能力。     

局部线性嵌入优化光谱回归的鲁棒人脸识别

针对高维小样本鲁棒人脸识别问题,提出了一种局部线性嵌入优化光谱回归算法。计算出训练样本的特征向量,然后用局部线性嵌入算法构建分类问题所需的嵌入,并学习每种分类的子流形所需的嵌入;利用光谱回归计算投影矩阵,最近邻分类器完成人脸的识别。在人脸数据库FERET、AR及扩展YaleB上的实验结果表明,相比其他几种光谱回归算法,该算法取得了更好的识别效果。     

破碎刚体三角网格曲面模型的特征轮廓线提取方法

区分了三角网格曲面模型上的三组概念：外表面与断裂面、内外边及内外轮廓线．运用内外边的性质快速提取了外轮廓线,采用法矢夹角计算和统计方法获取了模型上断裂面与原表面夹角的经验阈值及断裂面面积的上限,继而确定候选特征点集,最后利用扫描线算法获取包络于候选特征点集的内轮廓线．基于以上步骤提出三角网格曲面模型的特征轮廓线提取方法,该方法正确提取了破碎刚体模型的特征轮廓线,使空间曲线匹配得以进行．