自适应FCM算法在图像分割中的应用研究

针对目前还没有较好的方法确定模糊C均值FCM聚类中C值和各个初始聚类中心这一问题,提出一种先用进化聚类快速确定初始聚类中心和聚类个数C,后用模糊C均值FCM聚类的算法,算法时间复杂度和空间复杂度与C均值FCM基本相当。应用该算法在人物图像和遥感图像中进行了分割实验验证,算法在分割的准确性和模糊边界的分隔上取得令人满意的效果。     

基于改进模糊C均值聚类的光伏面板红外图像分割

红外图像具有对比度低和信噪比低等特点,这对红外光伏面板图像的分割始终是一个巨大的挑战.为了解决传统的模糊C均值（FCM）聚类算法易受到初始聚类中心不确定的影响和不考虑空间信息的问题,提出了一种基于模糊C均值改进的聚类分割算法,该算法利用直方图的特点确定初始聚类中心,同时在传统的模糊C均值（FCM）和模糊核C均值算法（KFCM）的基础上,利用像素之间的空间信息和邻域像素之间的关系改进传统FCM聚类目标函数,从而推导出新的目标函数.实验结果表明,该算法在分割质量和效果上与Otsu算法、文献[20]的自适应k-means算法及模糊核C均值算法（KFCM）相比,过分割和错分割率明显降低,且分割效果非常接近手动分割图.     

基于SSCL的模糊C均值图像分类方法

针对传统模糊C均值聚类方法对噪声敏感和过分依赖于初始聚类中心的缺点,提出基于SSCL的模糊C均值图像分类的自适应算法。该算法首先通过SSCL获得初始类别数和类别中心,然后作为模糊C均值聚类的输入,自动对图像进行分割,并对图像分割结果利用空间信息进行后处理。实验结果表明该方法较好地解决了FCM算法中的初始化和噪声敏感问题,具有较好的分类结果。     

基于均值漂移和小波变换的彩色图像分割方法

提出一种新的彩色图像分割算法,该算法利用均值漂移算法进行初始分割,利用其分割结果确定模糊C均值聚类(FCM)算法的初始聚类中心和聚类数目,以提高FCM算法的收敛速度;利用小波变换的多分辨率特性,实现图像由粗到细的图像分割。     

基于特征加权的自适应FCM彩色图像分割算法

图像分割是模式识别、图像理解、计算机视觉等领域的重要研究内容.基于模糊C均值聚类(FCM)的图像分割是应用较为广泛的方法之一,但其存在需预先给出初始聚类数目,且要考虑各个特征对分类的不同影响等问题.通过引入ReliefF技术进行特征加权,结合聚类有效性指数自适应确定初始聚类数目、根据Laws纹理测度提取图像特征等措施,提出了一种新的FCM彩色图像分割算法.实验结果表明,该算法可以有效地提高图像分割效果,分割结果优于现有FCM图像分割方案.     

结合爬山法的模糊C均值彩色图像分割方法

采用传统的模糊C均值聚类(FCM)算法进行彩色图像分割存在聚类数的选取、初始聚类中心的确定、迭代过程中的大计算量及后处理等问题。在对上述问题进行研究的基础上,针对传统FCM聚类分割时初始值选取方法的盲目性和随机性,为了更准确地自动获取待分割图像聚类的初始参数,提出了一种结合爬山法的模糊C均值彩色图像分割方法(HFCM),该方法可根据待分割图像的三维颜色直方图自适应地获取FCM算法的初始聚类中心及聚类数目,同时提出一种最频滤波与区域合并相结合的新的后处理策略,有效消除了小的空间区域。实验表明,相对于传统FCM,该图像分割方法的速度较快,并且分割结果更接近人类分割效果。     

基于粗糙集理论和FCM的图像聚类方法

提出一种将粗糙集方法与模糊C均值聚类(FCM)算法结合的图像聚类方法。借助于粗糙集理论在处理大数据量、消除冗余信息等方面的优点,减少模糊C均值聚类的训练数据量,克服其因为数据量大而处理速度慢等缺点,同时利用模糊C均值聚类好的聚类性能,对经过约简的最小属性子集进行聚类分析,实现图像聚类的快速、准确、鲁棒等优点。在人脸图像上的聚类实验取得了很好的效果。     

融合纹理特征的两阶段聚类分割算法

提出一种融合纹理特征的两阶段聚类分割算法。首先,选择纹理特征、差分均值和颜色分量这3个特征,组成一个分割所用的特征矢量;然后,使用直方图对特征矢量进行初始聚类中心和类别数的估算;最后,通过模糊C均值算法对特征矢量进行聚类。该算法有效地克服了模糊C均值(FCM)容易陷入局部最优的缺陷,使聚类结果更加精确。实验结果表明该方法比一些现存方法的分割效果要好。     

基于模糊C均值聚类的多分量彩色图像分割算法

以模糊C均值(FCM)聚类理论为基础,选用符合人眼视觉特性的HSI颜色空间,提出了一种新的多分量彩色图像分割算法。该算法首先结合数据分布特点确定出H分量与I分量的初始聚类中心;然后利用FCM聚类技术对H分量、I分量进行分类处理,以得到不同分量的像素点隶属度;最后,将所得到的不同分量像素点隶属度组织成2维特征,并以此进行模糊聚类图像分割。实验结果表明,该算法可有效提高图像分割效果,其分割结果优于传统FCM聚类图像分割方案。     

空间域与频域结合的FCM红外热像分割方法

由于成像特点及环境干扰,用于工件缺陷检测的红外热像通常较为模糊。为此,将空间域与频域结合的模糊C均值(FCM)聚类算法用于红外热像中缺陷及正常表面的分割。运用多种图像处理方法对原始红外热像进行预处理,将得到的高频图像及其邻域平均图像使用经典FCM聚类算法进行像素灰度的聚类。实验结果表明,该方法的分割效果较好。     

An adaptive unsupervised approach toward pixel clustering and color image segmentation

This paper proposes an adaptive unsupervised scheme that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. The algorithm, named Ant Colony-Fuzzy C-means Hybrid Algorithm (AFHA), adaptively clusters image pixels viewed as three dimensional data pieces in the RGB color space. The Ant System (AS) algorithm is applied for intelligent initialization of cluster centroids, which endows clustering with adaptivity. Considering algorithmic efficiency, an ant subsampling step is performed to reduce computational complexity while keeping the clustering performance close to original one. Experimental results have demonstrated AFHA clustering's advantage of smaller distortion and more balanced cluster centroid distribution over FCM with random and uniform initialization. Quantitative comparisons with the X-means algorithm also show that AFHA makes a better pre-segmentation scheme over X-means. We further extend its application to natural image segmentation, taking into account the spatial information and conducting merging steps in the image space. Extensive tests were taken to examine the performance of the proposed scheme. Results indicate that compared with classical segmentation algorithms such as mean shift and normalized cut, our method could generate reasonably good or better image partitioning, which illustrates the method's practical value.     

基于特征散度的自适应FCM图像分割算法

图像分割是模式识别、图像理解、计算机视觉等领域的重要研究内容。基于模糊C均值聚类(FCM)的图像分割是应用较为广泛的方法之一,但其存在距离测度鲁棒性差、需预先给出初始聚类数目、未考虑图像局部相关特性等问题。为克服上述缺点,通过引入特征散度进行距离测度,并结合聚类有效性指数自适应确定初始聚类数目和根据Laws纹理测度提取图像特征等措施,提出了一种新的FCM图像分割算法。实验结果表明,该新算法可以有效地提高图像的分割效果(特别是纹理图像),其分割结果优于现有FCM图像分割方案。     

An automatic fuzzy c-means algorithm for image segmentation

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.     

基于改进布谷鸟优化的模糊聚类图像分割

模糊C均值聚类算法(FCM)是一种应用非常广泛的聚类算法,但是它受初始聚类中心影响较大,容易陷入局部最优。 在标准布谷鸟算法(CS)的基础上 提出改进布谷鸟优化算法(ICS),将发现概率P由固定值转变成随迭代次数逐渐减小的变量,这样不仅可以提高搜索种群的质量,而且保证了算法的收敛。因此,可以将改进布谷鸟优化算法用于FCM算法聚类中心生成的过程(ICS_FCM),从而有效地避免FCM陷入局部最优。改进的算法具有良好的聚类效果和运行速度。实现基于改进布谷鸟优化的FCM图像分割,并与基于模拟退火的FCM算法(SA_FCM)进行对比。由实验结果可知,该算法(ICS_FCM)不仅取得了较好的分割效果,效率上也有明显的提高。     

Alpha-cut implemented fuzzy clustering algorithms and switching regressions.

In the fuzzy c-means (FCM) clustering algorithm, almost none of the data points have a membership value of 1. Moreover, noise and outliers may cause difficulties in obtaining appropriate clustering results from the FCM algorithm. The embedding of FCM into switching regressions, called the fuzzy c-regressions (FCRs), still has the same drawbacks as FCM. In this paper, we propose the alpha-cut implemented fuzzy clustering algorithms, referred to as FCMalpha, which allow the data points being able to completely belong to one cluster. The proposed FCMalpha algorithms can form a cluster core for each cluster, where data points inside a cluster core will have a membership value of 1 so that it can resolve the drawbacks of FCM. On the other hand, the fuzziness index m plays different roles for FCM and FCMalpha. We find that the clustering results obtained by FCMalpha are more robust to noise and outliers than FCM when a larger m is used. Moreover, the cluster cores generated by FCMalpha are workable for various data shape clusters, so that FCMalpha is very suitable for embedding into switching regressions. The embedding of FCMalpha into switching regressions is called FCRalpha. The proposed FCRalpha provides better results than FCR for environments with noise or outliers. Numerical examples show the robustness and the superiority of our proposed methods.     

快速模糊C均值聚类彩色图像分割方法

模糊C均值(FCM)聚类用于彩色图像分割具有简单直观、易于实现的特点，但存在聚类性能受中心点初始化影响且计算量大等问题，为此，提出了一种快速模糊聚类方法(FFCM)。这种方法利用分层减法聚类把图像数据分成一定数量的色彩相近的子集，一方面，子集中心用于初始化聚类中心点；另一方面，利用子集中心点和分布密度进行模糊聚类，由于聚类样本数量显著减少以及分层减法聚类计算量小，故可以大幅提高模糊C均值算法的计算速度，进而可以利用聚类有效性分析指标快速确定聚类数目。实验表明，这种方法不需事先确定聚类数目并且在优化聚类性能不变的前提下，可以使模糊聚类的速度得到明显提高，实现彩色图像的快速分割。     

一种新的聚类分析算法

给出了一种新的无监督聚类算法,但这种算法并非是基于目标函数的聚类算法,而是对数据直接设计一种迭代运算,以使数据在保持类特征的情况下进行重新组合最终达到分类的目的。通过对一类数据的实验表明,该算法在无监督给出类数方面具有较好的鲁棒性;另外,该算法在数据的准确归类、无监督聚类、确定性,以及对特殊类分布的适用性等方面均优于ＨＣＭ和ＦＣＭ算法,     

基于可靠性的鲁棒模糊聚类

相比于k-means算法,模糊C均值(FCM)通过引入模糊隶属度,考虑不同数据簇之间的相互作用,进而避免了聚类中心趋同性问题.然而模糊隶属度具有拖尾和翘尾的结构特征,因此使得FCM算法对噪声点和孤立点很敏感;此外,由于FCM算法倾向于将各数据簇均等分,因此算法对数据簇大小也很敏感,对非平衡数据簇聚类效果不佳.针对这些问...     

Study on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering

Fuzzy C-means (FCM) clustering has been widely used successfully in many real-world applications. However, the FCM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters. In this paper, a multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering (MFCM-TCSC) is provided. In this algorithm, the initial guesses of the locations of the cluster centers or the membership values are not necessary. Multi-centers are adopted to represent the non-spherical shape of clusters. Thus, the clustering algorithm with multi-center clusters can handle non-traditional curved clusters. The novel algorithm contains three phases. First, the dataset is partitioned into some subclusters by FCM algorithm with multi-centers. Then, the subclusters are merged by spectral clustering. Finally, based on these two clustering results, the final results are obtained. When merging subclusters, we adopt the lattice similarity method as the distance between two subclusters, which has explicit form when we use the fuzzy membership values of subclusters as the features. Experimental results on two artificial datasets, UCI dataset and real image segmentation show that the proposed method outperforms traditional FCM algorithm and spectral clustering obviously in efficiency and robustness.     

广义洛伦兹内核函数在模糊C均值聚类中的应用研究

模糊C均值(FCM)算法是数据聚类分析的主要算法。但在嘈杂环境下,对于抽样大小不一的聚类,数目越多准确性越低,上述弊端可通过替代性FCM(AFCM)的高斯内核映射来解决。鉴于AFCM的不足,提出了针对模糊C均值聚类的广义洛伦兹内核函数。利用该算法对鸢尾数据库进行聚类,将其划分成山鸢尾、变色鸢尾和维吉尼亚鸢尾3类。实验结果表明,广义洛伦兹模糊C均值(GLFCM)可实现对离群聚类和大小不等的聚类数据的分类,其结果优于K均值、FCM、替代性C均值(AFCM)、Gustafson-Kessel(GK)和 Gath-Geva(GG)方法,收敛迭代次数比AFCM的更少,其分区索引(SC)效果也好于其他方法。