Clustering ensemble via structured hypergraph learning

Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods.     

Binary classification in unstructured space with hypergraph case-based reasoning

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work with multiple data sources or in non-metric spaces, and accommodates with missing values. As a result, it drastically reduces the need for data preprocessing or feature engineering. Each element to be classified is partitioned according to its interactions with the training set. For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class.Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art. The time complexity is given and empirically validated. Its robustness with regard to hyperparameter sensitivity is studied and compared to standard classification methods. Finally, the limitation of the model space is discussed, and some potential solutions proposed.     

Supporting the design process with hypergraph genetic operators

In this paper an evolutionary technique is proposed as a method for generating new design solutions with genotypes represented in the form of hierarchical hypergraphs. Such hypergraph-based evolutionary design requires the adaptation of traditional evolutionary operators like cross-over and mutation. This paper presents an attempt at defining modified cross-over and mutation operators that act on hierarchical hypergraphs. The application of the proposed transformations is illustrated on examples of designing floor layouts and furniture with the use of the evolutionary method.     

Probabilistic hypergraph based hash codes for social image search

With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing（SH） has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.     

基于超像素的多主体图像交互分割

目的 为解决多主体图像的交互分割问题,在保证分割效果的前提上,提高分割的效率,达到实时交互修改分割结果的目的, 提出基于超像素的图像多主体交互分割算法.方法 基于图像的超像素构造一个多层流网络,利用用户交互绘制的简单笔画给出多主体分割的指导信息.流网络的边权值保证利用图割算法将图像分割成多个部分后,每个部分代表图像的一个主体.允许用户交互给出标记,实时修改分割结果,直到得到满意的多主体分割.结果 通过实验显示,本文方法能得到的满意多主体分割结果,而且时间效率较高.对分辨率为449×275的图像,算法能在1 s内给出结果,满足实时修改的要求.结论 基于超像素建立的图规模较小,能大大减少图割算法的运行时间,达到用户实时交互添加新笔画信息,交互地修正分割结果的目的.利用超像素的边界信息,用户只需输入比较简单的笔画信息,分割算法就能得到正确的多主体分割结果.     

Clustering spatial networks for aggregate query processing: A hypergraph approach

In spatial networks, clustering adjacent data to disk pages is highly likely to reduce the number of disk page accesses made by the aggregate network operations during query processing. For this purpose, different techniques based on the clustering graph model are proposed in the literature. In this work, we show that the state-of-the-art clustering graph model is not able to correctly capture the disk access costs of aggregate network operations. Moreover, we propose a novel clustering hypergraph model that correctly captures the disk access costs of these operations. The proposed model aims to minimize the total number of disk page accesses in aggregate network operations. Based on this model, we further propose two adaptive recursive bipartitioning schemes to reduce the number of allocated disk pages while trying to minimize the number of disk page accesses. We evaluate our clustering hypergraph model and recursive bipartitioning schemes on a wide range of road network datasets. The results of the conducted experiments show that the proposed model is quite effective in reducing the number of disk accesses incurred by the network operations.     

A repartitioning hypergraph model for dynamic load balancing

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static load balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic load balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan load balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic load balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic load balancing approaches, and multilevel approaches improve the repartitioning quality significantly.     

A hypergraph and perspective approach to organic synthesis design

Various computer programs for organic synthesis design greatly assist chemists in the task of synthesizing chemical compounds. Despite the value of such programs, however, their common and collective failing is that they disrupt the chemists' natural flow of thought, by interrupting their concentration on chemistry and repeatedly forcing them to attend to computing. This problem originates in the lack of a chemist's view among the various programs in the process of design. A new idea called hypergraph, with the concept of perspective in design, is proposed as a solution to the problem. The idea is designed to represent a dynamic perspective view over a graph-structured object, such as a chemical structure, and to support a perspective-oriented shift of view during organic synthesis design. An example of precursor generation for organic reaction design is presented to illustrate the desired capabilities of hypergraph during designing. The goal of the research is to create a modelling environment in which the processing of creative ideas is enhanced: for example, in organic synthesis design.     

基于超像素联接权模型的视频分割算法

视频图像分割是图像处理领域的一个热门问题。在传统分割算法的基础上,提出了一种新的无监督的视频分割算法。该算法采用超像素对运动前景进行表示,定义联接权概念来描述超像素属于同一物体的可能性,并利用当前帧的静态特征与前后帧的关联特征进行联接权计算。为优化超像素间匹配关系的搜索,算法引入了超像素颜色特征匹配约束与运动关联性匹配约束的机制。分别在简单场景和复杂场景进行了视频分割实验,简单场景下,算法保证了较高的召回率与稳定的准确率;复杂场景下,算法完成了人群中单个人的切分。大量实验结果表明,该算法能够实现视频图像的分割,并且能有效解决过分割问题。     

A polynomial time approximation scheme for embedding hypergraph in a weighted cycle

The problem of Minimum Congestion Hypergraph Embedding in a Weighted Cycle (MCHEWC) is to embed the hyperedges of a hypergraph as paths in a weighted cycle such that the maximum congestion is minimized. This problem is NP-hard. In this paper, we present a polynomial time approximation scheme (PTAS) for this problem.     

A computational model for ranking cloud service providers using hypergraph based techniques

In a cloud marketplace, the existence of wide range of Cloud Service Providers (CSPs) makes it hard for the Cloud Users (CUs) to find an appropriate CSP based on their requirements. The design of a suitable service selection framework helps the users in the selection of a suitable CSP, while motivating the CSPs to satisfy the assured Service Level Agreement (SLA) and enhance the Quality of Service (QoS). Existing service selection models employ random assignment of weights to the QoS attributes, replacement of missing data by random values, etc. which results in an inaccurate ranking of the CSPs. Moreover, these models have high computational overhead. In this study, a novel cloud service selection architecture, Hypergraph based Computational Model (HGCM) and Minimum Distance-Helly Property (MDHP) algorithm have been proposed for ranking the cloud service providers. Helly property of the hypergraph had been used to assign weights to the attributes and reduce the complexity of the ranking model, while arithmetic residue and Expectation–Maximization (EM) algorithms were used to impute missing values. Experimental results provided by MDHP under different case studies (dataset used by various research communities and synthetic dataset) confirms the ranking algorithm to be scalable and computationally attractive.     

A global parallel algorithm for the hypergraph transversal problem

We consider the problem of finding all minimal transversals of a hypergraph H⊆V², given by an explicit list of its hyperedges. We give a new decomposition technique for solving the problem with the following advantages: (i) Global parallelism: for certain classes of hypergraphs, e.g., hypergraphs of bounded edge size, and any given integer k, the algorithm outputs k minimal transversals of H in time bounded by polylog(|V|,|H|,k) assuming poly(|V|,|H|,k) number of processors. Except for the case of graphs, none of the previously known algorithms for solving the same problem exhibit this feature. (ii) Using this technique, we also obtain new results on the complexity of generating minimal transversals for new classes of hypergraphs, namely hypergraphs of bounded dual-conformality, and hypergraphs in which every edge intersects every minimal transversal in a bounded number of vertices.     

一种基于Multiway cut的多对象图像分割*

多对象分割是图像处理中的一个难题,基于Multiway cut的图像分割是一种人工交互式多对象分割方法,能够实现图像的粗分割和精确分割。使用分水岭分割图像,把图像分割为属性相似的小区域;根据交互建立节点层次图,构建带权无向网络;不同层次的节点参与不同的运算,采用Multiway cut迭代分割;交互和分割可以多次执行,直至满足用户的要求。实验结果表明,该方法人工参与方便,准确度得到提高,速度满足现场操作的要求。     

砂岩显微图像分析方法及其工具实现

图像分析是研究砂岩薄片的重要手段。研究适用于砂岩薄片的图像分析的方法并进行工具实现,在岩石学研究、油气勘探等方面具有重要意义。文中设计开发了砂岩显微图像分析软件系统。首先,提出基于超像素分割和聚类的图像分割方法来分割砂岩薄片显微图像,形成只具有单一矿物成分的超像素;然后,以矿物显微图像作为训练数据,提取颜色和局部等特征参数来训练分类器分类超像素;最后,合并相邻超像素从而形成完整的矿物颗粒,并标定其类别成分。在对方法进行研究的基础上,进行软件设计实现,对砂岩薄片显微图像中的矿物组分和组构特征进行分析。对一些采自西藏的典型砂岩薄片显微图像的分析表明,该方法具有良好的实用价值,但还需要进一步完善和优化。     

基于核图割模型的肝脏CT图像肿瘤分割

计算机断层成像(CT)对疾病的确诊意义重大,在医学图像的自动检测中应用较多的模型为图割模型,但传统图割算法严重依赖于对复杂区域进行大量建立的模型,运算复杂且不利推广。为此,在传统图割理论基础上引入核函数,提出一种基于核图割模型的肝脏CT图像肿瘤分割算法。通过核函数将原始数据映射到高维空间,并在高维图像数据空间用图割理论对CT图像的肝区与肿瘤区域进行分割,以提取疑似肿瘤区域,解决传统图割模型中需要依赖人机交互和对复杂区域建模困难等问题。由Mercer定理得出,核空间的点积运算不需要显式指定图像各区域的具体模型,进行核推广后克服了传统模型通用性不强的弱点。利用临床CT图像数据对该算法进行分割实验,结果表明,基于核推广后的图割算法能够有效对肿瘤和肝区进行分离,可应用于临床实际中作为肿瘤辅助诊断手段。     

基于改进SLIC算法的电力设备故障区域分割方法

在电力设备状态监测中,红外测温图像故障区域的分割是今后故障诊断智能化发展的关键环节。为了实现图像自动化处理,提高故障区域的分割精度,提出一种改进SLIC算法的故障区域分割方法。采用导向滤波器对红外测温图像进行预处理;在SLIC超像素迭代过程中增加亮度相似性限制条件,并将生成的超像素以种子点的色彩值匹配颜色;通过自动设置色调阈值,实现对电力设备故障区域的分割和标记。实验结果表明,改进的算法与原始SLIC算法相比,边缘召回率提高了4.10%,对故障区域的分割更具优势。     

Multi-level direct K-way hypergraph partitioning with multiple constraints and fixed vertices

K$K$-way hypergraph partitioning has an ever-growing use in parallelization of scientific computing applications. We claim that hypergraph partitioning with multiple constraints and fixed vertices should be implemented using direct K$K$-way refinement, instead of the widely adopted recursive bisection paradigm. Our arguments are based on the fact that recursive-bisection-based partitioning algorithms perform considerably worse when used in the multiple constraint and fixed vertex formulations. We discuss possible reasons for this performance degradation. We describe a careful implementation of a multi-level direct K$K$-way hypergraph partitioning algorithm, which performs better than a well-known recursive-bisection-based partitioning algorithm in hypergraph partitioning with multiple constraints and fixed vertices. We also experimentally show that the proposed algorithm is effective in standard hypergraph partitioning.     

基于无人机影像的烟草精细提取

烟草是一种特殊农作物,烟草的提取对其信息统计起着重要作用。针对烟草单株提取难的问题,提出了一种结合多特征和超像素的无人机影像烟草精细提取方法。首先利用简单线性迭代聚类 （Simple Linear Iterative Clustering, SLIC）算法对影像进行超像素分割;然后统计超像素的平均值、亮度、长宽比、形状指数、红绿蓝波段值和自定义植被指数;接着通过对超像素特征组合和特征阈值选取来实现烟草的精细提取;最后对提取信息进行统计和分析。实验结果表明：该方法能有效地提取烟草株树,准确度分别为99%和98.6%。利用该方法,在计算烟草产量方面供了有效参考,节省了大部分的人力财力。     

一种生物在体荧光成像的自适应分割算法

生物在体荧光成像是新兴分子影像技术中性能高、费用低、前景好的一种成像模态。针对生物在体荧光图像的特点和应用需求,提出一种全新的自适应图像分割算法。通过对荧光图像的归一化处理、连通性操作、感兴趣区域区分实现自适应分割。实验结果表明,该算法能够在弱信号、低信噪比、多光源的情况下得到较理想的分割结果,是一种有效的荧光图像分割算法。