基于部分背景加权更新的均值漂移跟踪算法

为了解决均值漂移跟踪算法中背景对目标定位的扰动, 提出了一种基于颜色和纹理混合特征以及采用背景加权更新的改进算法。改进算法先将原始视频序列RGB帧图像转换为HSV颜色空间表示, 然后分别在H、S通道上提取颜色特征, 在V通道上用LBP描述符提取纹理特征, 在此基础上为目标区域和背景区域建立三维颜色纹理混合直方图作为其描述符; 在对象的跟踪过程中, 通过巴氏系数选择性地加权更新部分背景信息。实验结果表明, 与基于全部背景更新策略相比, 改进算法充分利用了颜色和纹理特征并加权更新背景信息, 具有更高的可靠性和鲁棒性, 具有更好的计算效率。     

高斯颜色模型在瓷片图像分类中的应用

由于RGB颜色空间不能很好贴近人的视觉感知,同时也缺少对空间结构的描述,因此采用兼顾颜色信息和空间信息的高斯颜色模型以获取更全面的特征,提出了一种基于高斯颜色模型和多尺度滤波器组的彩色纹理图像分类法,用于瓷器碎片图像的分类。首先将原始图像的RGB颜色空间转换到高斯颜色模型;再用正规化多尺度LM滤波器组对高斯颜色模型的3个通道构造滤波图像,并借助主成分分析寻找主特征图,接着选取各通道的最大高斯拉普拉斯和最大高斯响应图像,与特征图联合构成特征图像组用以进行参数提取;最后以支持向量机作为分类器进行学习和分类。实验结果表明,与基于灰度的、基于RGB模型的和基于RGB_bior 4.4小波的方法相比,本文方法具有更好的分类结果,其中在Outex纹理图像库上获得的分类准确率为96.7%,在瓷片图像集上获得的分类准确率为94.2%。此方法可推广应用到其他彩色纹理分类任务。     

基于中值-游程共生矩阵的纹理特征提取

提出了一种基于中值-游程共生矩阵模型的纹理特征提取方法.该方法利用了图像的灰度信息和等灰度游程长度信息,通过计算图像的中值矩阵和游程矩阵,从而计算出中值-游程共生矩阵,来提取图像的特征.仿真结果表明,该方法能有效地分割出纹理图像上区域特性不同的纹理,且分割效果优于等灰度游程矩阵和灰度共生矩阵.     

Modeling and classifying symmetries using a multiscale opponentcolor representation

A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval     

Visual information retrieval system via content-based approach

In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.     

An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills,Sudan

The effectiveness of spectral and textural information in the identification of surface rock types in an arid region, the Red Sea Hills of Sudan, is evaluated using spectral information from the six Landsat TM optical bands and textural features derived from Shuttle Imaging Radar-C (SIR-C) C-band HH polarization data. An initial classification is derived from Landsat TM data alone using three classification algorithms, Gaussian maximum likelihood, a multi-layer feed-forward neural network and a Kohonen self-organizing feature map (SOM), to generate lithological maps, with classification accuracy being measured using a confusion matrix approach. The feed-forward neural net produced the highest overall classification accuracy of 57 per cent and was, therefore, selected for the second experiment, in which texture measures from SIR-C C-band HH-polarized synthetic aperture radar (SAR) data are added to selected TM spectral features. Four methods of measuring texture are employed, based on the Fourier power spectrum, grey level co-occurrence matrix (GLCM), multi-fractal measures, and the multiplicative autoregressive random field (MAR) model. The use of textural information together with a subset of the TM spectral features leads to an increase in classification accuracy to almost 70 per cent. Both the MAR model and the GLCM matrix approach perform better than Fourier and multi-fractal based methods of texture characterization.     

Performance analysis of textural features for characterization and classification of SAR images

A new method has been presented to compare the performance of textural features for characterization and classification of SAR (Synthetic Aperture Radar) images. In contrast to the conventional comparative studies based on classification accuracy, this method emphasizes the sensitivity of texture measures for grey level transformation and multiplicative noise of different speckle levels. Texture features based on grey level run length, texture spectrum, power spectrum, fractal dimension and co-occurrence have been considered. A number of image samples of built-up, barren land, orchard and sand regions were considered for the study. The interpretation of the results is expected to provide useful information for the remote sensing community, which employs textural features for segmentation and classification of satellite images.     

一种基于颜色基元共生矩阵的图像检索方法

提出一种结合图像颜色连通区域信息及其纹理特征的图像检索新方法。对图像进行分块,确定图像中的颜色连通区域集,提取图像中各颜色连通区域对应的4种颜色基元共生矩阵特征,针对该特征设计的图像相似性度量函数实现基于内容的图像检索。实验结果表明,该方法能有效地结合图像的纹理信息及其颜色构成和分布信息,具有良好的检索效果和性能。     

联合快舟一号影像纹理信息的城市土地覆盖分类

仅依靠光谱信息无法满足高分辨率遥感分类的应用需求,辅之以纹理特征信息进行分类,可提高影像分类精度。利用KZ\|1卫星影像和Landsat\|8卫星影像数据,基于面向对象的影像分割法和灰度共生矩阵纹理分析法对新疆石河子市局部城区进行了地表覆盖分类实验,将不同空间分辨率的全色影像纹理信息、光谱信息构成多种影像特征组合进行分类比较研究,以选择最佳的分类特征集。结果表明:KZ-1影像能为城市区域的土地覆盖分类提供丰富的纹理信息,面向对象的影像分割可较好地利用高分辨率数据的几何结构信息实现优化的影像分割,从而提高多光谱影像的分类精度,总体分类精度为90.06%,Kappa系数为87.93%,比单纯利用光谱信息分类的总体精度提高了8.02%,Kappa系数提高了9.65%,表明KZ\|1数据可为光谱分类提供丰富的纹理信息,从而提高城市区域的土地覆盖分类精度。     

基于四像素共生矩阵的图像检索

传统的灰度共生矩阵是一种有效的纹理图像分析方法,它在图像理解和计算机视觉研究领域已得到了广泛的应用。为了更有效地进行图像检索,提出了一种新型的共生矩阵描述子,它是通过描述4个像素的空间相关性来进行图像检索。利用该共生矩阵描述子进行图像检索时,首先在RGB颜色空间中计算彩色梯度,然后利用四像素共生矩阵来描述图像特征,并用于基于内容的图像检索。实验结果表明,四像素共生矩阵描述子能够结合颜色、纹理和形状特征,因此检索性能优于灰度共生矩阵和颜色相关图。     

面向对象分类方法在铁尾矿堆快速提取中的应用研究

以黄石市大冶铁矿区为例,利用面向对象分类方法进行铁尾矿堆信息快速提取试验研究。首先,根据WorldView-2影像特点,充分利用其丰富的光谱特征及精确的空间形状特征进行图像分割,突出影像对象边缘、重现地物实际存在情况;其次,分析影像对象的光谱、形状、纹理、拓扑关系等特征信息,建立分类规则进行分类,提取出尾矿堆信息。为了进一步提高分类精度,可以利用eCognition软件RS/GIS数据集成功能,在面向对象分类结果上进行目视解译。试验证明,面向对象分类方法适用于提取矿区尾矿堆信息,是高分辨率遥感影像自动分类的理想选择。     

基于SVM和纹理的煤和煤矸石自动识别

为提高煤矸石的自动识别和分选效率,提出了基于支持向量机(SVM)和纹理识别煤矸石的方法.选取两种煤和一种煤矸石的图像作为样本,经过图像预处理及图像灰度和纹理特征分析后,发现灰度均值、灰度共生矩阵最大值、二阶矩、对比度、相关、熵为有效特征.在此基础上,采用了支持向量机来完成图像的自动识别过程,选取上述6个参数作为支持向量机的训练特征,实验结果表明,该支持向量机识别煤和煤矸石的成功率较高.     

基于纹理和BP神经网络的SAR图像分类

研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明：纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。     

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city,Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.     

Image retrieval based on the texton co-occurrence matrix

This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of textons to describe image features. Image retrieval experimental results have shown that our proposed method has the discrimination power of color, texture and shape features, the performances are better than that of GLCM and CCG.     

Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram

A Semivariogram, as defined in geostatistics, is a powerful tool for texture extraction of remotely sensed images. However, the traditional texture features extracted by a semivariogram are generally for pixel-based classification. Moreover, most studies have been based on the original computation mode of semivariogram and discrete semivariance values. This article describes a set of semivariogram texture features (STFs) based on the mean square root pair difference (SRPD) to improve the accuracy of object-oriented classification (OOC) in QuickBird images. The adaptive parameters for the calculation of a semivariogram were first derived from semivariance analysis, including directions, moving window size, and lag distance. Then, 22 STFs were extracted from the discrete and mean/standard deviation semivariance, and 15 features were selected from the extracted STFs based on feature optimization. Then five grey-level co-occurrence matrix (GLCM) texture features (mean, homogeneity, contrast, angular second moment, and entropy) were calculated based on segmented image objects using the panchromatic band. A comparison of classification results demonstrates that the STFs described in this article are useful supplement information for the spectral OOC, and the spectral + STFs classification method can be used to obtain a higher classification accuracy than can the combination of spectral and GLCM features.     

基于色彩共生矩阵的CBIR方法

介绍了一种基于色彩共生矩阵提取颜色-纹理特征的图像检索方法。在灰度共生矩阵方法的基础上,发展出色彩共生矩阵方法,解决了灰度共生矩阵方法不能有效处理真彩色图像的缺陷,并从色彩共生矩阵中提取颜色和纹理特征用于图像检索。该方法易于实现、特征库简洁高效,且具有较好的检索效果。     

瓷砖图像的纹理特征分类研究

在已有的瓷砖图像分类系统中,仅靠颜色特征和简单的纹理边缘信息只能对无花纹的单色砖或简单花纹的瓷砖进行有效分类,对复杂图案的瓷砖存在识别率低的问题。针对此种情况,结合瓷砖图像的灰度共生矩阵和统计几何特征,将这些特征输入支持向量机进行特征分层分类。采用基于径向基核函数和[K]交叉验证法所得到的最优参数构造支持向量机,解决瓷砖纹理特征具有非线性的分类问题。用瓷砖生产线上采集的大量图像进行实验表明,该方法准确率高,分类效果好。     

基于组合特征提取与多级SVM的轮胎花纹识别

基于轮胎花纹分类识别在交通与刑事部门的重要作用,提出了一种新的基于组合特征提取与多级SVM的轮胎花纹识别方法。分别采用非下采样Contourlet变换和灰度共生矩阵方法提取轮胎花纹特征;组合两种方法所提取的特征作为图像特征,并从中提取5个有效特征作为最终识别特征;运用提取的5个特征和多级支持向量机分类器完成轮胎花纹的分类识别。新的特征提取方法所得轮胎花纹特征分离度高,用决策树SVM分类器预测分类效果理想,对轮胎花纹的正确分类识别有着重要意义。