Texture segmentation using hierarchical wavelet decomposition

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.     

一种基于小波变换的无监督纹理分割算法

提出了一种基于小波变换和均值偏移的无监督纹理图像分割算法。首先用小波变换对图像进行二级小波分解,然后用均值偏移算法估计出粗尺度上对应的聚类数目,并结合模糊c均值算法进行聚类,在此基础上,用定义的阈值函数和Fisher判据确定出细尺度上每个初始聚类中心的一个同组,从而实现图像的由粗到细的分割。实验结果表明,在分割精度相差不大的情况下,该方法解决了传统聚类方法所存在的需要聚类数目和对初始聚类中心敏感问题。     

利用Fisher线性判别细化K-均值聚类算法的灰度图像分割

聚类是图像分割的一种通用方法,K-均值法聚类图像分割具有一定的自适应性,但结果易受聚类中心和几何形状的影响。本文引入Fisher线性判别对K-均值分割的结果进行不断的迭代改进。实验结果表明该方法可以提高分割的精度和准确度。     

一种变换域纹理图像特征提取和分割方法

以对偶树复小波变换为基础,提出了一种提取纹理图像变换域统计特征,进而实现图像非监督分割的方法。该方法用Gamma分布和对数正态分布建模对偶树复小波系数的模值,将两种分布的参数综合起来作为像素特征,利用边缘保持的平滑技术（EPNSQ）进行特征平滑,最后使用K－均值聚类方法实现特征分类,得到图像的非监督分割结果。实验结果表明所提取的特征可以有效地表征不同的纹理,基于该特征得到了更为精确的图像分割结果。     

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。     

无监督纹理遥感图像分形分割仿真

针对无监督纹理遥感图像分形分割时,仅利用光谱信息,没有充分利用遥感图像中的纹理信息造成过分割或是欠分割的问题,提出结合光谱信息和纹理信息的无监督遥感图像分形分割方法。方法首先借助于双边滤波算法建立双边滤波模型,对无监督遥感图像进行平滑处理,利用边缘检测算子获取无监督遥感图像的纹理梯度和光谱梯度,保留遥感图像的纹理信息。将图像纹理梯度和光谱梯度的内积范数作为K-means算法的距离测度,针对K-means算法易陷入局部寻优问题,将遗传算法与K-means算法相结合实现全局寻优,完成无监督纹理遥感图像分形分割。通过在高分辨率遥感图像上的分割对比实验,证明上述方法能充分利用遥感图像的光谱和纹理信息,分割效果良好。     

基于分裂式K均值聚类的图像分割方法

模糊C均值聚类(FCM)算法是一种有效的无监督图像分割方法,适用于任意分类数,不需要预知图像特征,但其聚类效果直接受待分类样本噪声和分类初始条件的影响。因此,提出了一种适用于彩色图像分割的分裂式K均值聚类(FKM)算法,该算法首先使用中值滤波对分类样本去噪,然后使用一种分裂聚类法对图像样本进行预分类,得到一组样本集初始划分,最后以这组划分为起点,使用基于概率距离的K均值聚类对图像分割进行迭代优化。实验结果表明,该算法可以避免FCM的误分类,诸如陷于中心死区、中心重叠和局部极小值,而且提高了分割速度。     

基于免疫K-means聚类的无监督SAR图像分割

利用图像纹理的信息熵特征,并结合空间矩阵的概念,提出一种基于免疫K-means聚类的无监督SAR图像分割算法.免疫规划的K-means聚类克服收敛结果易陷于局部极值的缺点,且保持K-means算法快速收敛的特点.信息熵的应用可有效抑制相干斑噪声的影响,空间矩阵的引入实现聚类过程中类别的自动合并.该算法执行复杂度不高,对噪声的影响有较强的鲁棒性,分割结果较好,是一种实用的SAR图像分割算法.     

Markov random field models for unsupervised segmentation oftextured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation     

基于Contourlet变换和均值漂移的SAR图像分割

基于斑噪特性和纹理特征,提出了一种完全无监督的SAR图像分割算法。针对SAR图像的Contourlet变换,提出了子带选取的能量标准,对选定的子带计算能量特征和共生特征;依据特征向量的相似度剔除相近特征向量,用均值漂移算法获取纹理区域数和相应的中心特征,用像素的特征向量与相应中心特征向量的距离确定它们的分类。该文提出的方法不需要先验知识和训练样本。实验表明,基于Contourlet变换的均值漂移分割算法对混合Brodatz图像和SAR图像的分割取得了满意结果。     

基于随机权重粒子群和K-均值聚类的图像分割

K-均值聚类具有简单、快速的特点,因此被广泛应用于图像分割领域。但K-均值 聚类容易陷入局部最优,影响图像分割效果。针对K-均值的缺点,提出一种基于随机权重粒子 群优化(RWPSO)和K-均值聚类的图像分割算法RWPSOK。在算法运行初期,利用随机权重粒 子群优化的全局搜索能力,避免算法陷入局部最优;在算法运行后期,利用K-均值聚类的局部 搜索能力,实现算法快速收敛。实验表明：RWPSOK 算法能有效地克服K-均值聚类易陷入局 部最优的缺点,图像分割效果得到了明显改善;与传统粒子群与K-均值聚类混合算法(PSOK) 相比,RWPSOK 算法具有更好的分割效果和更高的分割效率。     

基于改进的K均值聚类彩色图像分割方法

提出了一种改进的K均值聚类图像分割方法。针对彩色图像的像素特征,利用Ohta等人的研究成果,选取能有效表示彩色像素特征的彩色特征集中的第一个分量作为图像像素的一维特征向量,用来替代经典K均值聚类图像分割中的灰度．大大降低了运算量。基于粗糙集理论的算法,求出初始聚类个数与均值。选用对特征空间结构没有特殊要求的特征距离代替欧氏距离,应用改进的K均值聚类算法对样本数据进行聚类,从而实现对彩色图像的快速自动分割。实验表明,该图像分割算法可有效提高图像分类的精度和准确度,并且运算代价小．收敛速度快。     

基于K均值聚类和粒子群优化的多核SVM图像分割

图像分割是图像理解和计算机视觉的重要内容.针对单核SVM在进行图像分割过程中不能兼顾分割精度高和泛化性能好的问题,提出一种基于K均值聚类和优化多核SVM的图像分割算法.该算法首先运用K均值聚类算法自动选取训练样本,然后提取其颜色特征和纹理特征作为训练样本的特征属性,并使用其对构造的多核SVM分割模型进行训练,最后用粒子群优化算法对多核核参数、惩罚因子以及核权重系数联合寻优,使生成的多核SVM具有更好的分割性能.实验结果表明,本文方法在有效提取图像目标细节的同时,获得了更高的分割精度,与基于单核的SVM分割模型相比,具有更强的泛化能力.     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.     

Gibbs Random Fields,Fuzzy Clustering,and the Unsupervised Segmentation of Textured Images

In this paper we present an unsupervised segmentation strategy for textured images, based on a hierarchical model in terms of discrete Markov Random Fields. The textures are modeled as Gaussian Gibbs Fields, while the image partition is modeled as a Markov Mesh Random Field. The segmentation is achieved in two phases: the first one consists of evaluating, from disjoint blocks which are classified as homogeneous, the model parameters for each texture present in the image. This unsupervised learning phase uses a fuzzy clustering procedure, applied to the features extracted from every pixel block, to determine the number of textures in the image and to roughly locate the corresponding regions. The second phase consists of the fine segmentation of the image, using Bayesian local decisions based on the previously obtained model parameters. The originality of the proposed approach lies in the three following aspects: (1) the Gibbs distribution corresponding to each texture type is expressed in terms of its canonical potential. This formulation leads to a compact formulation of the global field energy, in terms of the marginal probabilities over pixel cliques. A similar expression is also introduced in the partition model. Such formulations lead to the decomposition of the segmentation problem into a set of local statistical decisions; (2) the segmentation strategy consists of an unsupervised estimation, in which the model parameters are evaluated directly from the observation, by means of a fuzzy clustering technique; (3) no arbitrary assumption is made concerning the number of textures present. Rather, the fuzzy clustering procedure used to estimate the model parameters is applied in a hierarchical manner, searching for a cluster configuration of maximum plausibility.     

基于多子空间KL变换的纹理图像自监督分割方法

提出一种基于多子空间KL变换的纹理图像自监督分割方法。该方法将非监督聚类转变为有典型特征样本指导的自监督分类,解决误分类率高的问题。采用多子空间方法对样本进行特征选择,克服假设所有纹理特征都属于单个高斯分布的局限性。首先,对待分割图像进行多尺度、多方向的Gabor变换,使用模糊C均值方法从变换结果中提取具有典型性的样本作为训练样本;然后,使用训练样本为每一个类别生成一个单独的初始子空间;最后,采用多子空间KL变换,对其余样本在迭代过程中进行类别划分。实验结果证明,本文方法能够减少误分类率,改善分割效果。     

基于K-means聚类的数字半色调算法

数字半色调是在二值设备或多色二值设备上实现图像再现的一门技术,提出将K-means聚类法应用在数字半色调技术中。算法中应用人类视觉系统模型(HVS)和印刷模型最大限度减少原始灰度连续调图像和半色调图像之间的视觉误差;利用K-means聚类法将灰度图像划分成聚类分区,在每个聚类分区应用最小平方法(least-squares)最小化二值半色调图像和原始灰度级图像之间的平方误差,所构造的半色调算法与基于模型的最小平方法(LSMB)算法相比,随着聚类分区的增加,图像平滑且边缘清晰度增加,尤其是在图像细节部位。与LSMB算法比较,该算法的均方误差值有所降低,而权重信噪比和峰值信噪比提高了0.2～2 dB,模拟实验结果验证了算法的有效性。     

Dynamic clustering using particle swarm optimization with application in image segmentation

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the “best” number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The proposed approach was applied on both synthetic and natural images. The experiments conducted show that the proposed approach generally found the “optimum” number of clusters on the tested images. A genetic algorithm and random search version of dynamic clustering is presented and compared to the particle swarm version.     

基于K-均值与蚁群混合聚类的图像分割

针对单一聚类算法在图像分割中容易陷人局部最优或有过分割现象,造成分割精确度低等问题,文章提出了基于K-均值聚类和蚁群聚类相结合的新算法.新算法先将K-均值算法作快速分类,根据K-均值分类结果更新蚂蚁各路径上的信息素,指导其他蚂蚁选择,以提高蚁群聚类算法的运行效率.实验结果证明,新算法在图像分割处理的精确度上较单一的K均...     

结合纹理去除的遥感图像分割

针对包含复杂纹理信息的遥感图像难以进行精准图像分割的问题,提出了一种结合纹理去除的遥感图像分割方法。首先,改进了相对全变差纹理去除方法,通过引入新的范数约束使相对全变差纹理去除方法可以在去除纹理信息的同时凸显图像中的主要结构,达到辅助分割的效果;然后,使用均值漂移算法对经过纹理去除的遥感图像进行无监督聚类,达到分割的目的;最后,提出的遥感图像分割算法在不同遥感图像上进行了测试。实验结果表明,在高分辨遥感图像的分割上,所提算法可以分割出遥感图像中的主要目标,和直接分割或者结合其他纹理去除方法相比取得了更好的分割结果。所提出的分割算法可以降低纹理信息对图像分割的影响,提高遥感图像分割的精度。