一种基于纹理特征融合的SAR图像分割方法

在由若干灰度共生矩阵纹理统计量进行特征融合后所生成的图像上,定义多分辨双Markov-GAR模型,采用多分辨MPM参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法既利用了像素的灰度信息,也利用了像素的空间位置信息,削弱了斑点噪声对分割的影响。实验表明对于一些高分辨SAR图像,该方法与单纯基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,分割精度得以提高。     

Weighted partition consensus via kernels

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods.     

Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA

A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual least-squares support vector machine (LS-SVM) formulations. The formulation allows the extension to out-of-sample points. In this way, the proposed clustering model can be trained, validated, and tested. The clustering information is contained on the eigendecomposition of a modified similarity matrix derived from the data. This eigenvalue problem corresponds to the dual solution of a primal optimization problem formulated in a high-dimensional feature space. A model selection criterion called the Balanced Line Fit (BLF) is also proposed. This criterion is based on the out-of-sample extension and exploits the structure of the eigenvectors and the corresponding projections when the clusters are well formed. The BLF criterion can be used to obtain clustering parameters in a learning framework. Experimental results with difficult toy problems and image segmentation show improved performance in terms of generalization to new samples and computation times.     

基于邻域的多尺度模糊C-均值聚类图像分割

模糊C-均值(FCM)聚类是一种无监督聚类技术,广泛应用于图像分割。但它计算量偏大,且仅利用像素信息对像素进行聚类,而未使用空间分布信息。本文提出基于邻域信息的多尺度FCM聚类图像分割方法,该方法由小波分解得到图像多尺度框架,对框架顶层低分辨率图像的每个像素,用它的邻接像素引导它的聚类过程,来决定该像素的聚类,再由图像框架逐层对聚类分割进行细节修正。分割实验表明,该方法对比标准FCM分割,具有更好的抗噪性,且图像分割更均匀。     

基于随机取样的选择性K-means聚类融合算法

由于缺少数据分布、参数和数据类别标记的先验信息,部分基聚类的正确性无法保证,进而影响聚类融合的性能;而且不同基聚类决策对于聚类融合的贡献程度不同,同等对待基聚类决策,将影响聚类融合结果的提升。为解决此问题,提出了基于随机取样的选择性K-means聚类融合算法（RS-KMCE）。该算法中的随机取样策略可以避免基聚类决策选取陷入局部极小,而且依据多样性和正确性定义的综合评价值,有利于算法快速收敛到较优的基聚类子集,提升融合性能。通过2个仿真数据库和4个UCI数据库的实验结果显示：RS-KMCE的聚类性能优于K-means算法、K-means融合算法（KMCE）以及基于Bagging的选择性K-means聚类融合（BA-KMCE）。     

基于改进像素相关性模型的图像分割算法

图像分割是计算机视觉中的研究热点和难点.基于局部信息的模糊聚类算法(FLICM)在一定程度上提升了模糊聚类算法的鲁棒性,但噪声强度较大时无法获得较好的图像分割效果.针对传统的模糊聚类算法分割精度不佳等问题,提出了改进像素相关性模型的图像分割算法.首先通过分析像素的局部统计特征,设计了一种新型的像素相关性模型,在此基础上...     

SOM Ensemble-Based Image Segmentation

Image segmentation plays an important role in image analysis and image understanding. In this paper, an image segmentation method based on ensemble of SOM neural networks is proposed, which clusters the pixels in an image according to color and spatial features with many SOM neural networks, and then combines the clustering results to give the final segmentation. Experimental results show that the proposed method performs better than some existing clustering-based image segmentation methods.     

Adaptive spatial information-theoretic clustering for image segmentation

The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.     

结合FCMS与变分水平集的图像分割模型

提出了一个结合融合空间约束的模糊C均值（Fuzzy C means with spatial constraints,FCMS）聚类与变分水平集的图像模糊聚类分割模型.在该模型中引入了一个基于图像局部信息和空间信息的外部模糊聚类能量,从而可以获取精确的局部图像的空间特征,使得本文模型对噪声图像的聚类分割具有较强的鲁棒性.采用不同类型的实验图像,将本文模型与10个不同类型的图像分割模型进行了对比实验,实验结果显示本文模型能克服图像中噪声影响并取得较满意的聚类分割结果.     

Interactive color image segmentation via iterative evidential labeling

We develop an interactive color image segmentation method in this paper. This method makes use of the conception of Markov random fields (MRFs) and D–S evidence theory to obtain segmentation results by considering both likelihood information and priori information under Bayesian framework. The method first uses expectation maximization (EM) algorithm to estimate the parameter of the user input regions, and the Bayesian information criterion (BIC) is used for model selection. Then the beliefs of each pixel are assigned by a predefined scheme. The result is obtained by iteratively fusion of the pixel likelihood information and the pixel contextual information until convergence. The method is initially designed for two-label segmentation, however it can be easily generalized to multi-label segmentation. Experimental results show that the proposed method is comparable to other prevalent interactive image segmentation algorithms in most cases of two-label segmentation task, both qualitatively and quantitatively.     

基于数据特征的多传感器融合实时目标检测

为了进一步降低目标检测出现的误检率,提出了一种基于传感器数据特征的融合目标检测算法。首先,为了减少部分离群噪声点对点云表达准确性的影响,采用统计滤波器对激光雷达原始点云进行滤波处理;其次,为了解决点云地面分割在坡度变化时,固定阈值会导致分割不理想的问题,提出了自适应坡度阈值的地面分割算法;然后,建立KD（k-dimensional）树索引,加速DBSCAN（density-based spatial clustering of applications with noise）点云聚类,基于Andrew最小凸包算法,拟合最小边界矩形,生成目标三维边界框,完成聚类后的目标点云位姿估计;最后,将激光雷达检测到的三维目标点云投影到图像上,投影边界框与图像检测的目标边界框通过IoU关联匹配,提出基于决策级的三维激光雷达与视觉图像信息融合算法。使用KITTI数据集进行的测试实验表明,提出的点云聚类平均耗时降低至173 ms,相比传统的欧氏距离聚类,准确性提升6%。搭建硬件实验平台,基于实测数据的实验结果表明,提出的融合算法在目标误检率上比YOLO v4网络降低了约10%。     

基于相对密度和熵的混合属性聚类融合算法

混合属性聚类是近年来的研究热点,对于混合属性数据的聚类算法要求处理好数值属性以及分类属性,而现存许多算法没有很好得平衡两种属性,以至于得不到令人满意的聚类结果.针对混合属性,在此提出一种基于交集的聚类融合算法,算法单独用基于相对密度的算法处理数值属性,基于信息熵的算法处理分类属性,然后通过基于交集的融合算法融合两个聚类成员,最终得到聚类结果.算法在UCI数据集Zoo上进行验证,与现存k-prototypes与EM算法进行了比较,在聚类的正确率上都优于k-prototypes与EM算法,还讨论了融合算法中交集元素比的取值对算法结果的影响.     

基于核函数距离测度的加权模糊C均值聚类与Markov空域约束的快速鲁棒图像分割

由于模糊C均值聚类算法（FCM）存在两大缺陷：（1）对于非球形的数据聚类形式鲁棒性不够;（2）只考虑图像中的数值特征信息,而忽略了像素间的空间约束关系,因此FCM算法在对含有噪声的图像进行分割时缺乏足够的鲁棒性。针对以上问题,本文提出了一种结合Markov空域约束与基于核函数距离测度的加权模糊c均值聚类的快速鲁棒图像分割方法。为克服缺点（1）,我们使用基于核函数的距离测度取代FCM中的欧氏距离,并使用加权模糊聚类的方式保证了计算的简洁性。与此同时,我们用Markov随机场描述图像的空域约束信息,并且通过数据融合的方法将模糊分割结果与空域约束信息结合在一起,从而得到既包含像素数值特征又包括空域约束信息的图像分割场。这样既克服了传统模糊C均值聚类算法的缺点,又最大限度地保证了分割算法计算的简单有效性。     

基于变分水平集的图像模糊聚类分割

结合变分水平集方法和模糊聚类,提出了一个基于变分水平集的图像聚类分割模型.该模型引入了一个基于图像局部信息的外部模糊聚类能量和一个新的关于零水平集的正则化能量,使得该模型对噪声图像的聚类分割更具鲁棒性.通过在能量泛函中加入一个内部约束能量约束水平集函数为符号距离函数,可以使水平集演化过程无需重新初始化.进一步提出了一种变分形式的聚类中心更新方法,实现了半监督的图像聚类分割.实验中采用不同类型的图像与FCM聚类模型、CV模型、Samson模型进行了对比实验,实验结果显示,该模型能够克服图像中噪声的影响,取得较满意的聚类分割效果.     

Possibility Theoretic Clustering and its Preliminary Application to Large Image Segmentation

Rooted at the exponential possibility model recently developed by Tanaka and his colleagues, a new clustering criterion or concept is introduced and a possibility theoretic clustering algorithm is proposed. The new algorithm is characterized by a novel formulation and is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzen window technique and Yager’s probability–possibility transformation. Our experimental results demonstrate its success in artificial datasets and large image segmentation. In order to reduce the complexity of large image segmentation, we propose to integrate the new clustering algorithm with a biased sampling procedure based on Epanechnikov kernel functions. As demonstrated by the preliminary experimental results, the possibility theoretic clustering is effective in image segmentation and its integration with a biased sampling procedure offers an attractive framework of large image processing.     

A general framework for multiresolution image fusion: from pixels to regions

This paper presents an overview on image fusion techniques using multiresolution decompositions. The aim is twofold: (i) to reframe the multiresolution-based fusion methodology into a common formalism and, within this framework, (ii) to develop a new region-based approach which combines aspects of both object and pixel-level fusion. To this end, we first present a general framework which encompasses most of the existing multiresolution-based fusion schemes and provides freedom to create new ones. Then, we extend this framework to allow a region-based fusion approach. The basic idea is to make a multiresolution segmentation based on all different input images and to use this segmentation to guide the fusion process. Performance assessment is also addressed and future directions and open problems are discussed as well.     

基于无监督集成聚类的开放关系抽取方法

开放关系抽取(Open Relation Extraction, OpenRE)旨在从开放域语料库中抽取关系事实。大多数OpenRE方法通常局限于无监督方法提取命名实体之间的关系模式,然后将语义等价的模式聚类成一个关系簇,但由于缺少监督信息且聚类精度较低,影响了最终的关系抽取效果。为了进一步提高聚类性能,该文提出一种无监督集成聚类框架(Unsupervised Ensemble Clustering,UEC),它将无监督集成学习与基于信息度量的多步聚类算法相结合自主创建高质量伪标签,并以此作为监督信息改进关系特征的学习,从而引导聚类过程,获得更好的标签质量,最后通过多次迭代聚类发现文本中的关系类型。在FewRel和NYT-FB数据集上的实验结果表明,该文方法优于其他主流的基线OpenRE模型,F₁值分别达到了65.2%和67.1%。     

基于互信息选择聚类集成的网络流量分类方法

针对互联网流量标注困难以及单个聚类器的泛化能力较弱,提出一种基于互信息(MI)理论的选择聚类集成方法,以提高流量分类的精度。首先计算不同初始簇个数K的K均值聚类结果与训练集中流量协议的真实分布之间的规范化互信息(NMI);然后基于NMI的值来选择用于聚类集成的K均值基聚类器的K值序列;最后采用二次互信息(QMI)的一致函数生成一致聚类结果,并使用一种半监督方法对聚类簇进行标注。通过实验比较了聚类集成方法与单个聚类算法在4个不同测试集上总体分类精度。实验结果表明,聚类集成方法的流量分类总体精度能达到90%。所提方法将聚类集成模型应用到网络流量分类中,提高了流量分类的精度和在不同数据集上的分类稳定性。     

基于自适应邻域的鲁棒多视图聚类算法

针对现存的基于自适应邻域的多视图聚类算法没有考虑噪声和共识图信息损失的问题,提出一种基于自适应邻域的鲁棒多视图聚类（RMVGC）算法。首先,为了避免噪声和异常值对数据的影响,通过鲁棒主成分分析模型（RPCA）从原始数据中学习多个干净的低秩数据;其次,用自适应邻域学习直接融合多个干净的低秩数据来得到一个干净的共识关系图,从而减少图融合过程中的信息丢失。实验结果表明,所提RMVGC算法的标准化互信息（NMI）在MRSCV1、BBCSport、COIL20、ORL和UCI digits数据集上比目前流行的多视图聚类算法分别提升了5.2、1.36、27.2、4.66和5.85个百分点。同时,该算法保持了数据局部结构,增强了对原始数据的鲁棒性,提高了关系图质量,在多视图数据集上具有较好的聚类性能。     

DCT子空间的邻域加权模糊C均值聚类算法

模糊C均值聚类是一种有效的图像分割方法, 但存在因忽略空间上下文信息和结构信息而易为噪声所干扰的现象. 为此提出了DCT子空间的邻域加权模糊C均值聚类方法. 该方法首先结合分块的思想, 对图像块进行离散余弦变换(discrete cosine transform,DCT), 建立了一个基于图像块局部信息的相似性度量模型; 然后定义目标函数中的欧式距离为邻域加权距离; 最后将该方法应用于加噪的人工合成图像、自然图像和MR图像. 实验结果表明, 该方法能够获得较好的分割效果, 同时具有较强的抗噪性.