基于加速k均值的谱聚类图像分割算法改进

对谱聚类图像分割算法进行改进,即引入加速均值算法替换原算法中的k均值算法,得出加速谱聚类的图像分割算法.将改进算法应用于微软剑桥研究院Grab cut数据集中的5幅实验图像,结果显示:在平均区域一致性评价不降低的前提下,改进算法完成分割所花费的平均时间比改进前可缩短58％.     

用于彩图分割的自适应谱聚类算法

针对自调节谱聚类算法的缺陷,提出一种新的自适应谱聚类算法。它用全局平均N近邻距离作为比例参数σ,利用本征矢差异来估计最佳聚类分组数k,达到了比前者更好的效果,且更容易实现。在彩色图像分割实际应用中的实验结果表明,该算法适应性强、计算代价小、精度较高,性能好于或至少不差于以往的类似算法。     

具有自适应参数的粗糙k-means聚类算法

粗糙聚类是不确定聚类算法中一种有效的聚类算法,这里通过分析粗糙k-means算法,指出了其中3个参数w_l,w_u和ε设置时存在的缺点,提出了一种自适应粗糙k-means聚类算法,该算法能进一步优化粗糙k-means的聚类效果,降低对“噪声”的敏感程度,最后通过实验验证了算法的有效性。     

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.     

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.     

Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation

Image segmentation has been broadly applied in computer vision and image analysis. However, many segmentation methods suffer from limited accuracy for noisy images. To improve the robustness of the existing picture fuzzy clustering and solve the problem of selecting spatial constraint parameter, a novel picture fuzzy clustering is proposed. Firstly, a novel symmetric regularizing term is constructed to solve the time-consuming problem of existing picture fuzzy clustering, and the corresponding fuzzy clustering is proposed. Secondly, considering the correlation between current pixel and its neighboring pixels, the objective function is modified by adaptive weighting fusion of local mean information, and the maximum weight entropy constraint is embedded into it to solve the difficulty of parameter selection. Finally, the local spatial information constraint item of the current pixel is constructed by using its neighboring picture fuzzy partition information and is utilized to modify the picture fuzzy partition information of current pixel to correct the clustering center. Results show the proposed algorithm has some potential advantages in segmentation accuracy and anti-noise robustness.     

均值偏移法在核磁共振图象分割中的应用

文中采用均值偏移算法对加标记核磁共振(MR)图象的幅度谱进行模式分类构造低通滤波器,滤波后的频谱经傅立叶反变换后生成去网格标记的MR图象.然后运用Snake-GVF方法对结果图象进行分割,能够获得较好的左心室内膜边界.结果表明均值偏移算法应用于加标记MR图象分割是有效的.     

结合马尔可夫随机场与模糊C-均值聚类的脑MRI图像分割

脑磁共振成像(MRI)在临床上得到了大量的应用,准确分割脑组织结构可以提高脑疾病诊断的可靠性和治疗方案的有效性。模糊C-均值聚类(FCM)算法擅长解决图像中存在的模糊性和不确定性问题,是最常用的脑MRI分割方法。但因FCM仅利用图像灰度信息,没有考虑区域信息,导致其抗噪性能很差,常与区域信息结合进行改进。马尔可夫随机场(MRF)算法充分利用了图像区域信息,但容易出现过分割现象,因此FCM常与MRF进行结合改进。针对现有的FCM和MRF结合方式上存在的问题,提出了一种新型的自适应权值的FCM与MRF结合算法,用于脑MR图像分割。该算法利用了图像邻域像素的区域相关性,自适应的更新联合场的权值,改进了现有的权值固定的结合方式,充分发挥了FCM和MRF各自的优势,使二者结合更加合理。实验结果表明,本文算法较FCM和现存的一些FCM改进算法有更强的抗噪声能力和更高的分割精度。     

图像分割的改进的加权模糊聚类算法

利用模糊聚类算法对图像进行分割是一种比较经典的方法,但是标准的FCM算法并没有考虑像素的空间信息对聚类结果的影响。利用S函数将空间信息转为模糊聚类算法的目标函数的权值,从而使目标函数更合理。实验结果表明,改进算法较标准的FCM算法具有更好的分割效果。     

基于参考区域的k-means文本聚类算法

k-means是目前常用的文本聚类算法,该算法的主要缺点需要人工指定聚类的最终个数k及相应的初始中心点.针对这些缺点,提出一种基于参考区域的初始化方法,自动生成k-means的初始化分区,并且在参考区域的生成过程中,设计一种求最大斜率(绝对值)的方法确定自动阈值.理论分析和实验结果表明,该改进算法能有效的提高文本聚类的精度,且具有可行的效率.     

改进k-means聚类算法在图像检索中的应用研究

为了从大规模图像数据库中快速而准确地检索到所需图像,提出了一种结合图像特征索引库和平均面积直方图的方法。通过图像特征索引库减少对图像数据库的访问次数和访问数据量,实现对图像的快速检索。使用平均面积直方图方法,增强算法区分空间差异的能力,使得检索结果与人的视觉感受更加吻合。实验结果亦表明,该方法提高了图像检索的速度和精度。     

一种自适应的模糊C均值聚类图像分割方法

针对传统的模糊C均值聚类算法(FCM)在图像分割中对噪声十分敏感这一局限性,提出一种自适应的FCM图像分割方法。该方法充分考虑图像像素的灰度信息和空间信息,根据像素的空间位置自适应地计算一个合适的相似度距离来进行聚类分割图像。实验结果表明,与传统的FCM相比,该方法能显著提高分割质量,尤其是能提高对于图像噪声的鲁棒性和分割图像区域边缘的准确性。     

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.     

区间模糊谱聚类图像分割方法

近年来谱聚类算法在模式识别和计算机视觉领域被广泛应用,而相似性矩阵的构造是谱聚类算法的关键步骤。针对传统谱聚类算法计算复杂度高难以应用到大规模图像分割处理的问题,提出了区间模糊谱聚类图像分割方法。该方法首先利用灰度直方图和区间模糊理论得到图像灰度间的区间模糊隶属度,然后利用该隶属度构造基于灰度的区间模糊相似性测度,最后利用该相似性测度构造相似性矩阵并通过规范切图谱划分准则对图像进行划分,得到最终的图像分割结果。由于区间模糊理论的引入,提高了传统谱聚类的分割性能,对比实验也表明该方法在分割效果和计算复杂度上都有较大的改善。     

一种快速的模糊C均值聚类彩色图像分割方法

FCM用于彩色图像分割存在聚类数目需要事先确定、计算速度慢的问题,为此,提出一种快速的模糊C均值聚类方法（FFCM）。首先,对原始彩色图像进行基于梯度图的分水岭变换,从而把原始彩色图像数据分成一些具有色彩一致性的子集;然后,利用这些子集的大小和中心点进行模糊聚类。由于FFCM聚类样本数量显著减小,因此可以大幅提高模糊C均值聚类算法的计算速度,进而可以采用聚类有效性指标确定聚类数目。实验表明,这种方法不需要事先确定聚类数目,在聚类有效性能不变的前提下,可以使模糊聚类的速度得到明显提高,实现了彩色图像的快速分割。     

基于萤火虫优化的加权k-means算法*

针对传统k均值算法易受初始聚类中心和异常数据的影响等缺陷,利用萤火虫优化算法全局搜索能力强、收敛速度快的优势,对k均值算法的初始聚类中心进行优化,并通过引用一种加权的欧氏距离,减少异常数据等不确定因素带来的不良影响,提出了一种基于萤火虫优化的加权k均值算法。该算法在提升聚类性能的同时,有效增强了算法的收敛速度。在实验阶段,通过UCI数据集中的几组数据对该算法进行了分类实验及有效性测试,实验结果充分表明了该算法的有效性及优越性。     

基于机器学习的工业互联网入侵检测综述

DBSCAN(density-based spatial clustering of applications with noise)是应用最广的密度聚类算法之一. 然而,它时间复杂度过高(O(n²)),无法处理大规模数据. 因而,对它进行加速成为一个研究热点,众多富有成效的工作不断涌现. 从加速目标上看,这些工作大体上可分为减少冗余计算和并行化两大类;就具体加速手段而言,可分为6个主要类别：基于分布式、基于采样化、基于近似模糊、基于快速近邻、基于空间划分以及基于GPU加速技术. 根据该分类,对现有工作进行了深入梳理与交叉比较,发现采用多重技术的融合加速算法优于单一加速技术;近似模糊化、并行化与分布式是当前最有效的手段;高维数据仍然难以应对. 此外,对快速化DBSCAN算法在多个领域中的应用进行了跟踪报告. 最后,对本领域未来的方向进行了展望.

     

基于混沌粒子群和模糊聚类的图像分割算法*

模糊C-均值聚类算法(FCM)是一种结合模糊集合概念和无监督聚类的图像分割技术,适合灰度图像中存在着模糊和不确定的特点;但该算法受初始聚类中心和隶属度矩阵的影响,易陷入局部极小.利用混沌非线性动力学具有遍历性、随机性等特点,结合粒子群的寻优特性,提出了一种基于混沌粒子群模糊C-均值聚类(CPSO-FCM)的图像分割算法.实验证明,该方法不仅具有防止粒子因停顿而收敛到局部极值的能力,而且具有更快的收敛速度和更高的分割精度.     

Improved fuzzy clustering algorithm with non-local information for image segmentation

Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the fuzzy factor and could estimate the damping extent accurately. As a result, non-local context information can be utilized in the improved algorithm, which can improve the performance in restraining image artifacts. Experimental results on synthetic, medical and natural images show that the proposed algorithm performs better than current improved algorithms.     

An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

This paper presents an adaptive spatial information-theoretic fuzzy clustering algorithm to improve the robustness of the conventional fuzzy c-means (FCM) clustering algorithms for image segmentation. This is achieved through the incorporation of information-theoretic framework into the FCM-type algorithms. By combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the problems of sensitivity to noisy data and the lack of spatial information, and improve the image segmentation results. The experimental results have shown that this robust clustering algorithm is useful for MRI brain image segmentation and it yields better segmentation results when compared to the conventional FCM approach.