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。     

改进的自适应模糊ISODATA灰度图像分割算法

传统模糊ISODATA（Fuzzy ISODATA,FISODATA）算法中,分裂-合并操作需人工选取阈值参数。而不适当的阈值往往使算法陷入局部极值,因而得到错误的类属数并最终影响图像分割结果。为此,在模糊集理论基础上提出一种改进的自适应FISODATA算法。该算法设计了自适应分裂-合并操作,即在每次分裂-合并后,根据该次计算结果改变参数阈值,解决了人为选取参数带来的诸多问题。利用该算法对模拟图像和真实IKONOS图像进行分割实验,均能得到良好的分割结果。     

基于谱分解的模糊C均值算法在彩色图像分割中的应用

针对模糊C均值聚类算法对初始值敏感、易陷入局部最优以及谱聚类算法无法处理样本量过大的问题,提出了一种将模糊C均值聚类算法与谱聚类算法相结合的模糊谱聚类算法应用于彩色图像分割。大致分为三步,第一步对图像进行预处理,将颜色空间由RGB空间转换为Lab空间;第二步对特征空间进行冗余模糊C均值聚类算法得到冗余类;第三步由冗余类的隶属度矩阵和聚类中心矩阵得到冗余类的特征空间,并根据贴进度和传递闭包将该特征空间转换为冗余类的相似度矩阵进行谱聚类,完成冗余类的合并。实验结果表明,与模糊C均值聚类算法相比,模糊谱聚类算法对于初始值敏感问题、易陷入局部最优以及只能识别团状的蔟得到了很好的解决,从而使彩色图像分割结果更加合理。     

彩色图像分割的FCM初始化方法

针对传统模糊C-均值聚类方法所存在的过度依赖初始聚类中心、计算复杂度高等问题,提出一种新的FCM初始化方法.首先,使用维纳滤波分别对图像的R、G、B分量进行预处理,待转换为LAB色彩空间后,通过二次分水岭方法获取图像的封闭区域,并计算各区域的质心;其次,利用自适应无监督的方法对质心进行筛选和合并,将合并结果作为FCM的初始聚类中心;最后,使用FCM方法进行分割.实验结果表明,该方法不仅能够获得较准确的聚类中心,减少了迭代次数和运算时间,而且能够更好地实现图像的准确分割.     

应用于彩色图像分割的半监督多目标进化聚类算法*

为了有效的使用用户给定的先验信息,并从多个角度考虑图像分割问题,本文提出了应用于彩色图像分割的半监督多目标进化模糊聚类算法。首先,将半监督方法引入到多目标进化聚类算法中,通过使用少量的监督信息指导聚类过程;其次,将最大熵正则化引入到带有监督信息的目标函数中,使目标函数具有清晰的物理意义;最后,利用监督信息构造基于相似性度量的有效指标从非支配解集中选择一个最优解。实验结果表明,该算法与传统的多目标进化聚类算法及半监督模糊聚类算法相比具有更好的灵活性和准确性。     

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。     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

Robust color image segmentation using fuzzy c-means with weighted hue and intensity

In this paper we propose a novel method for color image segmentation. This method uses only hue and intensity components (which are chosen rationally) of image and combines those by adaptive tuned weights in a specially defined fuzzy c-means cost function. The tuned weights indicate how informative every color component (hue and intensity) is. Obtaining tuned weights begins with finding peaks of hue and intensity's histograms and continues by obtaining the table of the frequencies of hue and intensity values and computing entropy and contrast of every color component. Also this method specifies proper initial values for cluster centers with the aim of reducing the overall number of iterations and avoiding converging of FCM to wrong centroids. Experimental results demonstrate that our algorithm achieves better segmentation performance and also runs faster than similar methods.     

基于HSI颜色空间的X射线彩色图像分割

给出一种HSI颜色空间上基于HSI分量统计概率分布的彩色图像分割方法,并应用于X射线安全检查仪下危险品图像的分割。该方法将RGB彩色图像转换到HSI颜色空间,并以S分量为主要依据对图像进行粗分割,获得旅客包裹图像,然后,利用H和1分量的联合阈值进行细节分割获得疑似危险品的图像。在实际应用中表明该方法快速简单,满足实时性要求,是一种高效的自动算法。     

基于颜色特征的玉米雄穗图像分割

提出了一种基于颜色特征的玉米雄穗分割方法.利用侧抑制网络与二维Otsu结合的分割方法对玉米雄穗图像的YCbCr颜色空间的Cr分量进行分割,再利用相同方法对玉米雄穗图像的超绿特征图像进行分割,取两个分割结果的交集,去除小面积的连通域,得到玉米雄穗的分割图.为了验证算法的有效性,选用了不同生长环境的玉米雄穗图像,分别利用本文方法、二维Otsu和基于侧抑制的二维Otsu方法进行了比较实验.结果表明:该方法有很好的抗干扰性,对生长环境有很强的鲁棒性.     

基于模糊理论的图像分割方法

在图像信息隐藏研究领域,图像分割技术得到了广泛的应用.常用分割技术主观性较强,对图像的分割不够精确,利用模糊理论对图像进行分割可以有效解决这一问题.首先构造了图像的局部方差矩阵,然后根据模糊理论定义了平滑区、纹理区和边沿区3个模糊集,随后选取了适当的隶属函数并确定了其相关参数,科学、客观的确定了图像的分割阈值,进而实现了对图像的精确分割,最后通过实验数据验证了算法的有效性.     

Multiresolution color image segmentation

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed     

基于邻域的模糊C 均值图像分割算法*

给出了一种改进的模糊C-均值图像分割算法。该算法充分考虑了图像的空间信息,在图像存在噪声的情况下能产生区域一致的分割结果,并可以减少图像噪声。另外,通过引入聚类数目自动获取与聚类中心初始化的算法,一定程度上减少了算法的迭代次数。     

Iterative fuzzy image segmentation

The multispectral signature of features has been used for identification of objects in remotely sensed scenes for a number of years. Recently these techniques have been applied to feature selection in natural scenes. Due to the inherent noise and degradation of the input cues to the algorithms, meaningful image segmentation is a difficult process. In an effort to reduce the sensitivity of a system to these problems, we have been led to the development of a iterative fuzzy clustering technique for image segmentation. It is believed that this method represents an image segmentation scheme which can be used as a preprocessor for a multivalued logic based computer vision system.     

A new approach for color image segmentation based on color mixture

The aim of this paper is to propose a new methodology for color image segmentation. We have developed an image processing technique, based on color mixture, considering how painters do to overlap layers of various hues of paint on creating oil paintings. We also have evaluated the distribution of cones in the human retina for the interpretation of these colors, and we have proposed a schema for the color mixture weight. This method expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The color mixture generates planes that intersect the RGB cube, defining the HSM (Hue, Saturation, Mixture) color space. The position of these planes inside the RGB cube is modeled, based on the distribution of r, g and b cones of the human retina. To demonstrate the applicability of the proposed methodology, we present in this paper, the segmentation of “human skin” or “non-skin” pixels in digital color images. The performance of the color mixture was analyzed by a Gaussian distribution in the HSM, HSV and YCbCr color spaces. The method is compared with other skin/non-skin classifiers. The results demonstrate that our approach surpassed the performance of all compared methodologies. The main contributions of this paper are related to a new way for interpreting color of binary images, taking into account the bit-plane levels and the application in image processing techniques.     

基于HSI空间模糊聚类的彩色图像分割新方法

模糊C均值(FCM)被广泛应用于彩色图像分割中,但传统的模糊C均值由于没有考虑空间信息,因此对噪声特别敏感。针对此问题,提出了一种在HIS颜色空间结合像素邻域空间信息的模糊聚类新方法。实验结果表明,此方法对高噪声图像有较好的处理结果。     

基于SLIC的改进GrabCut彩色图像快速分割

GrabCut算法用户交互量少且分割精度高,但它迭代使用GraphCuts的求解模式使得在处理高分辨率图像时,耗时巨大。提出了一种快速GrabCut算法,在高斯混合模型参数估计过程中,通过SLIC算法构建精简的GraphCuts模型以实现加速。通过SLIC算法将原始图像快速地预分割成具有确定边界且区域内相似度高的超像素图,并以此构建精简的网络图。以块内的RGB均值描述超像素特征进行高斯混合模型参数估计。为了提高分割精度,使用得到的GMM参数对原始图像进行分割。实验结果证明了该算法在时效和精度上都有很好的性能。     

将Otsu用于多阈值彩色图像分割的方法及优化

针对传统Otsu算法只用于单阈值分割的不足,将Otsu算法推广到多阈值彩色图像分割中,提出先在众多极大值中寻找有意义峰值,根据峰值将直方图划分成多个待分割区间,再在每个区间进行阈值选取的方法;并且综合运用了形态学的方法对分割结果进行优化,降低阈值法因不考虑图像空间特性而造成的对噪声敏感的影响。实验结果表明,该方法能自动而快速地对彩色图像进行多阈值分割,而且具有较强的抗噪能力。     

基于核模糊聚类的彩色图像色彩-纹理分割方法

提出了一种利用色彩-纹理综合特征进行彩色图像分割的新方法。首先利用HSI色彩模型提取图像的色彩信息,其次,采用与方向无关的Gabor变换对彩色图像的强度信息进行处理,提取图像的纹理基元。根据上述过程所获得的色彩-纹理特征,采用核模糊聚类方法实现彩色图像的自动分割。实验结果表明,所提出的方法综合利用了图像的色彩、纹理信息,使彩色图像的分割结果更准确。