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...     

快速鲁棒核空间模糊聚类分割

目的 传统模糊C-均值聚类应用于图像分割仅考虑像素本身的聚类问题,无法克服噪声干扰对图像分割结果的影响,不利于受到噪声干扰的工业图像、医学影像和高分遥感影像等进行目标提取、识别和解译。嵌入像素空间邻域信息或局部信息的鲁棒模糊C-均值聚类分割算法是近年来图像分割理论研究中的热点课题。为此,针对现有的鲁棒核空间模糊聚类算法非常耗时且抑制噪声能力弱、不适合强噪声干扰下大幅面图像快速分割等问题,提出一种快速鲁棒核空间模糊聚类分割算法。方法 利用待分割图像中像素邻域的灰度信息和空间位置等信息构建线性加权滤波图像,对其进行鲁棒核空间模糊聚类。为了进一步提高算法实时性,引入当前聚类像素与其邻域像素均值所对应的2维直方图信息,构造一种基于2维直方图的鲁棒核空间模糊聚类快速分割最优化数学模型,采用拉格朗日乘子法获得图像分割的像素聚类迭代表达式。结果 对大幅面图像添加一定强度的高斯、椒盐以及混合噪声,以及未加噪标准图像的分割测试结果表明,本文算法比基于邻域空间约束的核模糊C-均值聚类等算法的峰值信噪比至少提高1.5 dB,误分率降低约5%,聚类性能评价的划分系数提高约10%,运行速度比核模糊C-均值聚类和基于邻域空间约束的鲁棒核模糊C-均值聚类算法至少提高30%,与1维直方图核空间模糊C-均值聚类算法具有相当的时间开销,所得分割结果具有较好的主观视觉效果。结论 通过理论分析和实验验证,本文算法相比现有空间邻域信息约束的鲁棒核空间模糊聚类等算法具有更强的抗噪鲁棒性、更优的分割性能和实时性,对大幅面遥感、医学等影像快速解译具有积极的促进作用,能更好地满足实时性要求较高场合的图像分割需要。     

一种快速鲁棒核空间图形模糊聚类分割算法

针对现有鲁棒图形模糊聚类算法难以满足强噪声干扰下大幅面图像快速分割的需要，提出一种快速鲁棒核空间图形模糊聚类分割算法。该算法将欧氏空间样本通过核函数映射至高维空间；采用待分割图像中像素邻域的灰度和空间等信息构建线性加权滤波图像，对其进行鲁棒核空间图形模糊聚类；并引入当前聚类像素与其邻域像素均值所对应的二维直方图信息，获得鲁棒核空间图形模糊聚类快速迭代表达式。对大幅面图像添加高斯和椒盐噪声进行分割测试，实验结果表明:本文算法相比基于图形模糊聚类等分割算法的分割性能、抗噪鲁棒性和实时性有了显著提高。     

Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation

Suppressed fuzzy c-means clustering algorithm (S-FCM) is one of the most effective fuzzy clustering algorithms. Even if S-FCM has some advantages, some problems exist. First, it is unreasonable to compulsively modify the membership degree values for all the data points in each iteration step of S-FCM. Furthermore, duo to only utilizing the spatial information derived from the pixel’s neighborhood window to guide the process of image segmentation, S-FCM cannot obtain satisfactory segmentation results on images heavily corrupted by noise. This paper proposes an optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation to solve the above drawbacks of S-FCM. Firstly, an optimal-selection-based suppressed strategy is presented to modify the membership degree values for data points. In detail, during each iteration step, all the data points are ranked based on their biggest membership degree values, and then the membership degree values of the top r ranked data points are modified while the membership degree values of the other data points are not changed. In this paper, the parameter r is determined by the golden section method. Secondly, a novel gray level histogram is constructed by using the self-tuning non local spatial information for each pixel, and then fuzzy c-means clustering algorithm with the optimal-selection-based suppressed strategy is executed on this histogram. The self-tuning non local spatial information of a pixel is derived from the pixels with a similar neighborhood configuration to the given pixel and can preserve more information of the image than the spatial information derived from the pixel’s neighborhood window. This method is applied to Berkeley and other real images heavily contaminated by noise. The image segmentation experiments demonstrate the superiority of the proposed method over other fuzzy algorithms.     

几何距优化质心结合隶属度约束RFCM的脑MRI图像分割算法

针对现有图像分割算法聚类复杂以及分割精度不够高的问题,提出了基于几何距优化质心和粗糙模糊C-均值（RFCM）相结合的医学图像聚类分割算法。首先建立软集表示的像素集,并计算每个像素与质心之间的距离,然后基于像素和质心之间的最小距离,将像素分组到聚类中。为了将软集应用到粗糙模糊C-均值中,定义了一个模糊软集,进一步将输入图像转换为二值图像,通过计算连通区域的几何距选择适当的质心。最后利用这些新的质心计算更新像素的隶属度值,从而完成模糊聚类划分。在Allen Brain Atlas等三个医学数据库上评估了所提出混合算法的性能,获得的Jaccards系数和分割精度（SA）都优于几种对比算法。实验证明,提出的聚类分割算法具有良好的性能。     

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.     

双中心组合迭代抑制式模糊C-均值聚类图像分割算法

针对抑制式模糊C-均值聚类算法应用于灰度图像分割时出现收敛速度较慢和像素误判的问题,通过挖掘图像同质区域内像素间的相关性与分析像素位置对类别判定的影响,提出一种双中心组合迭代抑制式模糊C-均值聚类图像分割算法.首先在图像上经选点、扩展、提取等环节优选出较好的初始聚类中心;然后按该中心分别查找图像中灰度值与其相等的像素位置并遴选产生隐藏中心;其次采用负指数函数对像素位置与隐藏中心之间的欧氏距离进行归一化,得到位置特征;接着在对该特征赋权后直接修正模糊划分矩阵;最后结合抑制式思想进一步减少算法的迭代次数.与现有的多种相关算法进行对比,实验结果表明,所提出算法在获得致密且分离性较好聚类的同时,能够改善图像分割的准确率和执行效率.     

结合类内和类间距离的可能聚类分割算法

目的 为了进一步提高噪声图像分割的抗噪性和准确性,提出一种结合类内距离和类间距离的改进可能聚类算法并将其应用于图像分割。方法 该算法避免了传统可能性聚类分割算法中仅仅考虑以样本点到聚类中心的距离作为算法的测度,将类内距离与类间距离相结合作为算法的新测度,即考虑了类内紧密程度又考虑了类间离散程度,以便对不同的聚类结构有较强的稳定性和更好的抗噪能力,并且将直方图融入可能模糊聚类分割算法中提出快速可能模糊聚类分割算法,使其对各种较复杂图像的分割具有即时性。结果 通过人工合成图像和实际遥感图像分割测试结果表明,本文改进可能聚类算法是有效的,其分割轮廓清晰,分类准确且噪声较小,其误分率相比其他算法至少降低了2个百分点,同时能获得更满意的分割效果。结论 针对模糊C-均值聚类分割算法和可能性聚类分割算法对于背景和目标颜色相近的图像分类不准确的缺陷,将类内距离与类间距离相结合作为算法的测度有效的解决了图像分割归类问题,并且结合直方图提出快速可能模糊聚类分割算法使其对于大篇幅复杂图像也具有适用性。     

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,...     

鲁棒空间约束的模糊聚类图像分割

目的 为进一步提高分割精度,在模糊聚类的基础上引入统计信息,提出一种鲁棒型空间约束的模糊聚类分割算法。方法 基于局部空间信息的先验概率与后验概率,提出一种新型空间约束项,并通过卷积操作提高运行效率;进而引入负对数联合概率作为测度函数,进一步提高算法对于各像素点所属类别的甄别能力;同时将测度函数与空间约束项整合至目标函数中,通过迭代更新各参数达到最小化目标函数的目的。结果 对于合成图像的实验结果表明,本文算法对于噪声类型和噪声强度具有较强的鲁棒性;对于彩色图像的实验结果表明,在适当的特征描述符的辅助下,本文算法也能够获得令人满意的分割结果和较高的分割精度。结论 本文算法克服了现有算法的缺陷,进一步提升了图像的分割精度。其适用于分割带噪声图像,且在适当纹理特征的辅助下分割彩色图像,与同类算法的比较实验结果验证了本文算法的有效性。     

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.     

基于空间模式聚类最大熵图像分割算法研究

研究图像分割优化问题,在分割图像中,提取信息受到各种因素影响,分割效果不理想。针对图像分割计算复杂,造成图像分割分辨率低,清晰度不高。同时,当图像中的信息量非常大时,图像分割非常耗时。为了有效地分割图像,提出了一种基于空间模式聚类和最大熵算法原理相结合的图像分割方法。首先对图像采用最大熵算法进行图像分割,为每个熵区域定义特征量。根据不同的特征量计算相似区域之间的欧氏距离和空间距离,从而确定像素聚类中心的距离。然后对分割后的图像区域采用基于空间模式聚类方案进行合并,并对图像进行二值化处理。仿真表明与传统图像分割相比,提高了分割效率,分割出的图像边缘效果清晰,证明了算法的可行性和有效性。     

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

不同形状邻域空间信息的模糊聚类图像分割

在经典的融合空间信息的模糊聚类图像分割方法中,图像像素的空间信息大,都采用正方形的邻域窗来获取。为了更好地分割出图像中的边界及细节信息,对不同形状邻域空间信息的模糊聚类图像分割进行了研究。在该方法中,首先采用圆形、三角形和菱形邻域窗获得图像像素的空间信息,然后分别将这三种空间信息引入到融合空间信息的模糊聚类图像分割中。Berkeley图像上的分割实验表明分别采用圆形、三角形和菱形邻域窗获得图像像素空间信息的模糊聚类图像分割方法在分割性能上要优于融合正方形邻域窗空间信息的方法。     

自适应烟花算法下多维模糊C均值彩色图像分割

针对图像分割在自然场景中,分割精度不高和细节保持不够敏感,提出一种自适应烟花算法下的多维模糊C均值彩色图像分割算法。结合动态时间弯曲思想,以邻域像素相似特点构造弯曲曲线,得到多维相似距离和新的目标函数。在自适应烟花寻优算法下,找到最优聚类中心,最终达到对图像分割效果。实验表明,该算法与同类算法相比,对彩色图像有良好的分割效果,对图像的细节保持也不错。     

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.     

模糊相关图割的非监督层次化彩色图像分割

目的 基于阈值的分割方法能根据像素的信息将图像划分为同类的区域,其中常用的最大模糊相关分割方法,因能利用模糊相关度量划分的适当性,得到较好的分割结果,而广受关注。然而该算法存在划分数需预先确定,阈值的分割结果存在孤立噪声,无法对彩色图像实施分割的问题。为此,提出基于模糊相关图割的非监督层次化分割策略来解决该问题。方法 算法首先将图像划分为若干超像素,以提高层次化图像分割的效率;随后将快速模糊相关算法与图割结合,构成模糊相关图割2-划分算子,在确保分割效率的基础上,解决单一阈值分割存在孤立噪声的问题;最后设计了自顶向下层次化分割策略,利用构建的2-划分算子选择合适的区域及通道,迭代地对超像素实施层次化分割,直到算法收敛,划分数自动确定。结果 对Berkeley分割数据库上300幅图像进行了测试,结果表明算法能有效分割彩色图像,分割精度优于Ncut、JSEG方法,运行时间较这两种方法也提高了近20%。结论 本文算法为最大模糊相关算法在非监督彩色图像分割领域的应用提供指导依据,能用于目标检测和识别领域。     

融合双特征图信息的图像显著性检测方法

目的 图像的显著性检测是将图像中最重要的、包含丰富信息的区域标记出来,并应用到图像分割、图像压缩、图像检索、目标识别等重要领域。针对现有研究方法显著性目标检测结果不完整以及单一依靠颜色差异检测方法的局限性,提出一种综合图像底层颜色对比特征图和图像颜色空间分布特征图的显著性检测方法,能够有效而完整地检测出图像中的显著性区域。方法 本文方法结合了SLIC超像素分割和K-means聚类算法进行图像特征的提取。首先,对图像进行SLIC（simple linear iterative clustering）分割,根据像素块之间的颜色差异求取颜色对比特征图;其次,按照颜色特征对图像进行K-means聚类,依据空间分布紧凑性和颜色分布统一性计算每个类的初步颜色空间分布特征。由于聚类结果中不包含空间信息,本文将聚类后的结果映射到超像素分割的像素块上,进一步优化颜色空间分布图;最后,通过融合颜色对比显著图和图像颜色空间分布特征图得到最终的显著图。结果 针对公开的图像测试数据库MSRA-1000,本文方法与当前几种流行的显著性检测算法进行了对比实验,实验结果表明,本文方法得到的显著性区域更准确、更完整。结论 本文提出了一种简单有效的显著性检测方法,结合颜色对比特征图和图像颜色空间分布特征图可以准确的检测出显著性区域。该结果可用于目标检测等实际问题,但该方法存在一定的不足,对于背景色彩过于丰富且与特征区域有近似颜色的图像,该方法得到的结果有待改进。今后对此算法的优化更加侧重于通用性。     

基于马氏距离的FCM图像分割算法

基于模糊C均值聚类的图像分割是应用较为广泛的方法之一,但大多数模糊C均值聚类方法都是基于欧式距离,且存在运算时间过长等问题。提出了一种基于Mahalanobis距离的模糊C均值聚类图像分割算法。实验分析表明,提出的算法在保证分割质量的前提下,能较快提高分割速度。实验结果表明了该方法的有效性。     

基于有偏场的光栅图像模糊聚类分割算法

针对有偏场环境下带有光栅的散焦图像分割问题,提出了一种新的基于有偏场估计的模糊聚类分割算法。通过建立依赖于有偏场的模糊聚类目标函数,导出了基于灰度以及邻域灰度均值的聚类中心、模糊聚类函数以及有偏场估计的迭代算法;并在该算法生成的初始分割基础上,利用膨胀算子对分类结果进行细化。该方法较好地处理了传统模糊聚类对有偏场下光栅图像分割精度下降的问题。实验结果表明,基于有偏场的模糊聚类算法能有效分割光栅图像,其分割精度优于传统模糊聚类和阈值法。