Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.     

基于混合邻域约束项的改进FCM算法

传统模糊聚类算法在影像分割过程中仅考虑影像的光谱信息,所以对噪声比较敏感.对此,提出基于混合邻域约束项的改进模糊C均值聚类(MNCFCM)算法.首先,从隶属性及光谱属性两方面定义邻域像素关于中心像素的相似度;然后,利用线性加权的方式将从两方面定义的相似度进行融合,同时结合邻域像素到聚类中心的欧氏距离构造混合邻域约束项,并将其引入目标函数中,以平衡影像分割过程中的影像平滑及细节保留,实现对影像的更优分割;最后,通过对合成影像及真实遥感影像分割结果的定性、定量评价,验证所提出算法具有较强的鲁棒性,在降低对噪声的敏感性的同时,能够较好地保留影像细节,获得高精度的分割结果.     

一种基于超像素和生成对抗网络的视网膜血管分割方法

针对传统视网膜图像血管分割中部分血管轮廓粗糙、血管末梢和分支细节丢失等问题,提出 一种结合线性谱聚类超像素与生成对抗网络(Generative Adversarial Networks,GAN)的视网膜血管分割 方法。该方法首先对 GAN 进行改进,采用空洞空间金字塔池化模块的多尺度特征提取来提高 GAN 分 割精度,在获得视网膜血管分割图像后,利用线性谱聚类超像素分割的边缘贴合性高、轮廓清晰的特 点,将 GAN 输出图像映射到超像素分割图再对像素块进行分类,以达到分割的效果。仿真实验结果表 明,与传统的视网膜血管分割方法相比,该方法在灵敏度和准确性上有一定提升,轮廓边缘细节方面 有着更好的效果。     

基于遗传优化谱聚类的图形分割方法

传统的谱聚类方法使用k-means达到最后的聚类目的。k-means对初始条件敏感,易陷入局部最优,从而导致传统的谱聚类方法应用到图像分割时效果不太理想。将遗传算法用于优化谱方法的聚类阶段,提出一种以遗传算法优化普聚类的图像分割方法(Image Segmentation Algorithm of Spectral Clustering Optimization Based on Genetic,ISCOG)。在合成图像与真实图像上的实验表明ISCOG算法极大地提高了谱聚类算法的稳定性和聚类质量,证明了ISCOG算法的优越性。     

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。     

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...     

基于免疫谱聚类的图像分割

提出了一种基于免疫谱聚类的图像分割方法.利用谱聚类的维数缩减特性获得数据在映射空间的分布,在此基础上构造一种新的免疫克隆聚类,用于在映射空间中对样本进行聚类.该方法通过谱映射为后续的免疫克隆聚类提供低维而紧致的输入.而免疫克隆聚类算法具有快速收敛到全局最优并且对初始化不敏感的特性,从而可以获得良好的聚类结果.在将其用于图像分割时,采用了Nystr?m逼近策略来降低算法复杂度.合成纹理图像和SAR图像的分割结果验证了免疫谱聚类算法用于图像分割的有效性.     

融合快速全局K-means与区域合并的图像分割

提出一种融合快速全局K-means与区域合并的图像分割方法。该方法利用中值滤波方法对图像去噪;运用快速全局K-means算法对图像的颜色空间进行聚类分析;结合区域合并准则,对初始分割合并得到最终的分割结果。实验表明,与同类算法比较,该方法的分割结果在图像细节方面能够很好地满足人的主观视觉。     

Robust path-based spectral clustering

Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings. Our proposed method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. In particular, color images from the Berkeley segmentation data set and benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness.     

基于鱼群算法优化normalized cut的彩色图像分割方法

为了克服传统的谱聚类算法求解normalized cut彩色图像分割时,分割效果差、算法复杂度高的缺点,提出了一种基于鱼群算法优化normalized cut的彩色图像分割方法.先对图像进行模糊C-均值聚类预处理,然后用鱼群优化算法替代谱聚类算法求解Ncut的最小值,最后通过最优个体鱼得到分割结果.实验表明,该方法耗时少,且分割效果好.     

基于图划分的图像直方图聚类分割

传统的基于图论的图像分割方法都是直接对图像灰度数据进行聚类分割,算法计算量较大。提出一种新的基于图论的直方图聚类分割算法,算法对图像直方图数据进行聚类,并由此得到分割阈值。由于输入值为直方图数据而不是图像灰度,数据量最大为 256而与像素数无关。实验研究表明,本方法在分割质量基本不变的情况下使得计算量大为减少。     

基于半监督的超像素谱聚类彩色图像分割算法

近年来谱聚类算法被广泛应用于图像分割领域,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,提出了基于半监督的超像素谱聚类彩色图像分割算法。该算法利用超像素将彩色图像进行预分割,利用用户提供的少量标记信息构造预分割区域的基于半监督的模糊相似性测度,利用该相似性测度构造预分隔区域的相似性矩阵并通过规范切图谱划分准则对预分割区域进行划分得到最终的图像分割结果。由于少量标记信息和模糊理论的引入,提高了传统谱聚类的分割性能,对比实验也表明该算法在分割效果和计算复杂度上都有较大的改善。     

A hierarchical evolutionary algorithm for automatic medical image segmentation

Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a hierarchical evolutionary algorithm (HEA) is proposed for medical image segmentation. The HEA can be viewed as a variant of conventional genetic algorithms. By means of a hierarchical structure in the chromosome, the proposed approach can automatically classify the image into appropriate classes and avoid the difficulty of searching for the proper number of classes. The experimental results indicate that the proposed approach can produce more continuous and smoother segmentation results in comparison with four existing methods, competitive Hopfield neural networks (CHNN), dynamic thresholding, k-means, and fuzzy c-means methods.     

基于改进的相似度度量的谱聚类图像分割方法

针对传统谱聚类图像分割方法存在分割准确度不够高的缺点,提出一种基于改进的相似度度量的谱聚类图像分割方法。该方法首先使用超像素分割算法将图像预分割为一定数目的超像素集合,并构建以超像素为节点的图;然后融合超像素的协方差描述子、颜色信息、纹理信息、梯度信息以及边缘信息作为超像素的特征来度量超像素间的相似性,进而得到超像素的相似度矩阵;最后使用NJW算法对超像素图进行分割。大量的实验结果验证表明,改进的分割方法在分割精度上优于目前存在的无监督分割方法,并且在交互式分割的模式下,该方法可以准确分割出用户指定的目标。     

基于空间特征的谱聚类含噪图像分割

为克服传统谱聚类算法应用到含噪图像分割时易受到图像中噪声影响的问题,提出一种基于空间特征的谱聚类含噪图像分割算法。该方法利用图像各个像素的灰度信息、局部空间邻接信息及非局部空间信息设计像素的三维特征,通过引入空间紧致性函数建立像素特征点与其K个最近邻之间的相似性,进而利用谱聚类算法得到图像的最终分割结果。实验中采用含噪的人工图像、自然图像及合成孔径雷达图像与空间模糊聚类、规范切谱聚类和Nystrm方法3种算法进行对比实验,实验结果验证文中方法能克服图像中噪声影响并取得较满意的分割效果。     

Spectral clustering with fuzzy similarity measure

Spectral clustering algorithms have been successfully used in the field of pattern recognition and computer vision. The widely used similarity measure for spectral clustering is Gaussian kernel function which measures the similarity between data points. However, it is difficult for spectral clustering to choose the suitable scaling parameter in Gaussian kernel similarity measure. In this paper, utilizing the prototypes and partition matrix obtained by fuzzy c-means clustering algorithm, we develop a fuzzy similarity measure for spectral clustering (FSSC). Furthermore, we introduce the K-nearest neighbor sparse strategy into FSSC and apply the sparse FSSC to texture image segmentation. In our experiments, we firstly perform some experiments on artificial data to verify the efficiency of the proposed fuzzy similarity measure. Then we analyze the parameters sensitivity of our method. Finally, we take self-tuning spectral clustering and Nyström methods for baseline comparisons, and apply these three methods to the synthetic texture and remote sensing image segmentation. The experimental results show that the proposed method is significantly effective and stable.     

Weighted graph cuts without eigenvectors a multilevel approach

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.     

基于K-均值改进蚁群优化的彩色图像边缘检测算法

为提高图像边缘检测的精度,提出一种基于K-均值改进蚁群优化（ACO）的彩色图像边缘检测算法。将聚类嵌入到边缘检测中,使这2类图像分割方法有效结合,增强了2类方法的优势。实验结果表明,该算法有效解决了传统蚁群算法（ACO）收敛较慢的问题,较好地保留了图像边缘细节,降低了计算复杂度,与典型分割方法相比具有更好的性能。     

参考1维光谱差异的区域生长种子点选取方法

目的 区域生长法是遥感影像分割中常用的算法,该算法首先选取适当的像元作为生长的起始点（种子点）。现有的种子点选取方法存在种子点数目较多、效率低以及地物细节种子点不足等问题。针对种子点选取存在的问题,提出一种基于1维光谱差异的区域生长种子点的选取方法。方法 首先计算像元间1维（水平、竖直）方向上的光谱差异,然后选取光谱差异的局部极小值作为种子点,最后对种子点进行优选,得到区域生长的起点。结果 应用本文方法选取种子点,对高分辨率的IKONOS遥感影像进行了区域生长。将实验结果与分形网络演化方法及Kernel Graph Cuts方法的分割结果进行了目视对比,并且分别计算了3种方法所得分割结果的基元内部同质性和基元间相关性的评价指数。目视比较的结果表明,本文的种子点选取方法能够为区域生长提供具有代表性的种子点,得到了精细的分割结果。在定量评价上,本文方法也表现出了数值优势,各波段分割质量指数均提高15%以上。结论 提出的种子点选取方法能够为高分辨率遥感图像的区域生长分割提供具有代表性的种子点,产生精细的分割图像,对于地物细节有良好的分割效果,具有较高的实用价值。     

改进的K均值聚类算法彩色图像分割的研究

针对传统K均值聚类算法在彩色图像分割中受K值和初始聚类中心影响较大等问题。在基于图像子块划分的基础上给出了一种k值和初始聚类中心确定方法,并用区域生长算法对聚类后的子块进行块后处理,利用提出的算法对多幅自然图像进行了分割实验,并与相似的分割方法进行了比较实验,给出了详细的实验结果与分析。实验表明该方法分割速度快,效果好,具有较高的实用价值。