Shape recognition from large image libraries by inexact graph matching

This paper describes a graph-matching technique for recognising line-pattern shapes in large image databases. The methodological contribution of the paper is to develop a Bayesian matching algorithm that uses edge-consistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node feature-vectors are constructed by computing normalised histograms of pairwise geometric attributes. Attribute similarity is assessed by computing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the database which has the largest a posteriori probability.     

基于相似图投影学习的多视图聚类

随着数据来源方式的多样化发展,多视图聚类成为研究热点。大多数算法过于专注利用图结构寻求一致表示,却忽视了如何学习图结构本身;此外,一些方法通常基于固定视图进行算法优化。为了解决这些问题,提出了一种基于相似图投影学习的多视图聚类算法(multi-view clustering based on similarity graph projection learning, MCSGP),通过利用投影图有效地融合了全局结构信息和局部潜在信息到一个共识图中,而不仅是追求每个视图与共识图的一致性。通过在共识图矩阵的图拉普拉斯矩阵上施加秩约束,该算法能够自然地将数据点划分到所需数量的簇中。在两个人工数据集和七个真实数据集的实验中,MCSGP算法在人工数据集上的聚类效果表现出色,同时在涉及21个指标的真实数据集中,有17个指标达到了最优水平,从而充分证明了该算法的优越性能。     

Aggregation based on graph matching and inexact coarse grid solve for algebraic two grid

A graph matching is used to construct aggregation-based coarsening for an algebraic two-grid method. Effects of inexact coarse grid solve is analysed numerically for a highly discontinuous convection–diffusion coefficient matrix, and for problems from the Florida matrix market collection. The proposed strategy is found to be more robust compared to a classical algebraic multi-grid approach based on strength of connections. Basic properties of two-grid method are outlined.     

AGraP: an algorithm for mining frequent patterns in a single graph using inexact matching

Knowledge and Information Systems - Frequent graph mining algorithms commonly use graph isomorphism to identify occurrences of a given pattern, but in the last years, a few works have focused on...     

An inexact interior-point method for system analysis

In this article, a primal-dual interior-point algorithm for semidefinite programming that can be used for analysing e.g. polytopic linear differential inclusions is tailored in order to be more computationally efficient. The key to the speedup is to allow for inexact search directions in the interior-point algorithm. These are obtained by aborting an iterative solver for computing the search directions prior to convergence. A convergence proof for the algorithm is given. Two different preconditioners for the iterative solver are proposed. The speedup is in many cases more than an order of magnitude. Moreover, the proposed algorithm can be used to analyse much larger problems as compared to what is possible with off-the-shelf interior-point solvers.     

基于加权特征投影的信源数估计方法

考虑到色噪声或低快条件下噪声特征值发散,导致基于特征分解的信源数估计方法得到的信号判据值和噪声判据值区分不明显,提出了一种基于加权特征投影的信源数估计方法;首先,为了使该方法可适用于低信噪比条件,对阵列接收数据的协方差矩阵进行降噪处理,并利用降噪后协方差矩阵所有特征值和特征向量构造了一个用来区分信号和噪声的加权空间矩阵;然后,将降噪后的协方差矩阵在该加权空间矩阵上投影,从而增大了信号判据值与噪声判据值的差异;最后,结合幂函数的缩放性构建了判决函数,进而实现信源数估计;通过理论分析和实验验证,该方法不仅适用于白噪声和色噪声条件,而且在低快拍和低信噪比条件下优势明显,在快拍数为10,信噪比分别为0 dB的白噪声和6 dB的色噪声条件下,该方法的成功检测概率均达到90％以上,同时该算法在信源数较多时效果鲁棒.     

Median graph computation for graph clustering

In this paper, we are interested in the problem of graph clustering. We propose a new algorithm for computing the median of a set of graphs. The concept of median allows the extension of conventional algorithms such as the k-means to graph clustering, helping to bridge the gap between statistical and structural approaches to pattern recognition. Experimental results show the efficiency of the new median graph algorithm compared to the (only) existing algorithm in the literature. We also show its effective use in clustering a set of random graphs and in a content-based synthetic image retrieval system.     

基于上界单纯形投影图张量学习的多核聚类算法

近年来,多核图聚类（MKGC）受到了广泛的关注,这得益于多核学习能有效地避免核函数与核参数的选择,而图聚类能充分挖掘样本间的复杂结构信息。然而现有的MKGC方法存在着如下问题：图学习技术使得模型复杂化,图拉普拉斯矩阵的高秩特性使其难以保证学到的关系图包含精确的c个连通分量（块对角性质）,以及大部分方法忽略了候选关系图间的高阶结构信息,使得多核信息难以被充分利用。针对以上问题,提出了一种新的MKGC方法。首先,提出一种新的上界单纯形投影图学习方法,直接将核矩阵投影到图单纯形上,降低了计算复杂度;同时,引入一种新的块对角约束,使学到的关系图能保持精确的块对角属性;此外,在上界单纯形投影空间中引入低秩张量学习来充分挖掘多个候选关系图的高阶结构信息。在多个数据集上与现有的MKGC方法相比,所提出方法计算量小、稳定性高,在聚类精度（ACC）和标准互信息（NMI）指标上具有较大的优势。     

Accumulator-based inexact matching using relational summaries

We are building a system that can rapidly determine the pose of a known object in an unknown view using a view class model of the object. The system inputs a three-dimensional CAD model; converts it to a three-dimensional vision model that contains the surfaces, edges, vertices, and topology of the object; and uses the vision model to determine the view classes or representative views of the object. In this paper we define therelational pyramid structure for describing the features in a particular view or view class of an object and thesummary structure that is used to summarize the relational information in the relational pyramid. We then describe an accumulator-based method for rapidly determining the view class(es) that best match an unknown view of an object.This research was partially supported by the National Aeronatics and Space Administration (NASA) through a subcontract from Machine Vision International.     

基于投影直方图匹配的双目视觉跟踪算法

为了在机器人机械手双目视觉伺服系统中跟踪并精确定位目标的空间位置,提出了一种利用投影直方图匹配和极线几何约束的目标跟踪方法。分别在2个视觉中人工标定目标,并提取目标在多颜色空间的水平、垂直投影直方图作为匹配模板;在一个视觉中利用目标的运动一致性原则和投影直方图匹配搜索并跟踪目标;在另一个视觉中依据双目视觉系统的极线几何原理限定目标搜索范围,搜索并定位目标。该方法利用水平、垂直投影直方图描述目标的结构信息,同时完成了双目视觉系统中的目标跟踪与配准功能,有利于目标的精确定位和视觉测量。实验结果表明,该方法可在双目视觉系统中有效跟踪目标,运算效率高,鲁棒性强。     

An exact method for graph coloring

We are interested in the graph coloring problem. We propose an exact method based on a linear-decomposition of the graph. The complexity of this method is exponential according to the linearwidth of the entry graph, but linear according to its number of vertices. We present some experiments performed on literature instances, among which COLOR02 library instances. Our method is useful to solve more quickly than other exact algorithms instances with small linearwidth, such as mug graphs. Moreover, our algorithms are the first to our knowledge to solve the COLOR02 instance 4-Inser_3 with an exact method.     

基于图聚类的入侵检测算法研究

针对当前聚类算法仅依赖于初始聚类中心并且无法精确区别非凹形状类的不足,现将图学习知识应用到聚类算法中,提出了一种基于图聚类的入侵检测算法P-BFS。为得到较准确的分类模型,算法中引入了一种基于逼近函数的相似性度量方法。实验结果论证了图聚类思想应用于入侵检测系统的优越性;同时表明了,与K-means聚类算法相比,P-BFS图聚类算法具有较高的性能。     

基于图模块度聚类的异常检测算法

社会网络的数据规模在不断扩大,现存的异常检测算法对复杂社会网络进行检测的效果不理想,提出了一种基于图模块度聚类的异常检测算法(anomaly detection algorithm based on graph modularity clustering, GMC＿AD),该算法适用于解决受网络规模以及复杂度的限制导致检测效率不高的问题。GMC＿AD算法在分析网络拓扑结构的基础上,通过引入异常节点加权机制和模块度聚类算法进行异常检测。GMC＿AD算法主要在三个方面进行改进：a)设计网络中节点演化的量化策略,以此识别具有异常演化行为的节点来得到异常节点集合;b)通过模块度聚类的方法降低网络规模;c)在计算网络波动值的过程中使用加权机制合理考虑异常节点的影响,再通过网络波动值变化来检测异常。基于真实社会网络VAST、EU＿E-mail和ENRON进行对比实验,GMC＿AD算法准确地检测出异常发生的时段,实验结果显示在事件检测敏感性上提高了50%～82%,在异常检测运行效率上提高了30%～70%。实验结果表明,GMC＿AD算法不仅提高了异常检测算法的准确率和敏感性,还提高了异常检测算法的效率...     

基于图相似匹配的政务网络结构一致性验证方法

针对政务网络结构的一致性验证问题,提出一种基于图相似匹配的政务网络结构一致性验证方法。方法首先抽象出政务网络的图模型,利用网络结构的模块化特征和顶点的k-邻近关系进行可扩展的图相似匹配,获取两图之间的所有相似结构;然后引入节点重要性贡献和路径距离衰减因素,提出一种改进的图相似性度量函数,计算网络结构之间的一致性程度。实验结果表明,该方法能够准确、有效地评估政务网络结构的一致性程度,细粒度地反映网络结构之间的相似差异情况,包括网络拓扑与系统部署存在的各种违规情况。     

An improved graph cut algorithm in stereo matching

In stereo matching tasks, the matching effect is often very poor when the texture of the region is weak or repeated. To solve this problem, an improved Graph Cut stereo matching algorithm based on Census transform is proposed. The Hamming distance of the corresponding pixels in the left and right images after Census transform is introduced as the similarity measure in the data term of the energy function. In this way, the dependence on the pixel value is reduced. The stereo matching experiments are implemented on the standard images of Middlebury stereo benchmark and the real scene images, and it demonstrates that our algorithm is robust and can obtain better performance in weak texture or repeated texture region.     

An eigendecomposition approach to weighted graph matching problems

An approximate solution to the weighted-graph-matching problem is discussed for both undirected and directed graphs. The weighted-graph-matching problem is that of finding the optimum matching between two weighted graphs, which are graphs with weights at each arc. The proposed method uses an analytic instead of a combinatorial or iterative approach to the optimum matching problem. Using the eigendecompositions of the adjacency matrices (in the case of the undirected-graph-matching problem) or Hermitian matrices derived from the adjacency matrices (in the case of the directed-graph-matching problem), a matching close to the optimum can be found efficiently when the graphs are sufficiently close to each other. Simulation results are given to evaluate the performance of the proposed method     

An optimal method for data clustering

An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).     

Exact and inexact LQG model matching problems

The design of discrete-time LQG (linear-quadratic-Gaussian) controllers that yield closed-loop systems that match a provided model is addressed. The solution is in the form of a two-degree-of-freedom controller having feedback and feedforward paths that independently provide the optimal actions for disturbance rejection and model matching. Exact and approximate model matching cases are discussed in a unified fashion