Multi-structure local binary patterns for texture classification

Recently, the local binary patterns (LBP) have been widely used in the texture classification. The LBP methods obtain the binary pattern by comparing the gray scales of pixels on a small circular region with the gray scale of their central pixel. The conventional LBP methods only describe microstructures of texture images, such as edges, corners, spots and so on, although many of them show good performances on the texture classification. This situation still could not be changed, even though the multi-resolution analysis technique is adopted by LBP methods. Moreover, the circular sampling region limits the ability of the conventional LBP methods in describing anisotropic features. In this paper, we change the shape of sampling region and get an extended LBP operator. And a multi-structure local binary pattern (Ms-LBP) operator is achieved by executing the extended LBP operator on different layers of an image pyramid. Thus, the proposed method is simple yet efficient to describe four types of structures: isotropic microstructure, isotropic macrostructure, anisotropic microstructure and anisotropic macrostructure. We demonstrate the performance of our method on two public texture databases: the Outex and the CUReT. The experimental results show the advantages of the proposed method.     

基于多分辨率的厄米高斯矩的LBP纹理分类

针对LBP（局部二值模式)纹理描述子局限于在单一分辨率下捕获纹理图像的纹理信息的问题,提出一种基于多分辨率的厄米高斯矩的LBP纹理分类方法。首先结合图像纹理的多分辨率特性,采用厄米高斯矩对图像进行多分辨率重构,然后利用LBP纹理描述子对重构图像进行特征提取,最后采用K近邻特征空间距离的分类方法进行纹理分类。选取KTH-TIPS纹理数据库的纹理图像进行测试实验,实验结果表明,与传统LBP纹理分类方法相比,使用多分辨率的厄米高斯矩的LBP纹理分类方法进行纹理分类,可以更加全面地描述图像的纹理信息,使纹理分类准确率更高。     

基于LBP值对空间统计特征的纹理描述符

针对基于内容图像检索应用背景下局部二值模式(LBP)描述符缺乏空间描述能力及所需特征矢量维数较长的不足, 提出一种基于LBP值对空间统计特征构建的改进纹理描述符(ILBP)。ILBP描述符首先利用LBP微模式编码方法将原始图像转换为LBP伪灰度图像, 然后再提取出多个关于LBP值对空间分布关系统计值构成描述图像特征的特征矢量。在基于内容的图像检索原型测试平台上完成大量实验。实验结果表明, 与LBP及其各类变种描述符相比, ILBP描述符在进一步增强LBP描述符描述能力的同时大幅度压缩特征矢量维数, 具有更好的查询正确率和查询效率。     

基于词包和特征融合的目标识别算法

针对现有词包模型对目标识别性能的不足,对特征提取、图像表示等方面进行改进以提高目标识别的准确率。首先,以密集提取关键点的方式取代SIFT关键点提取,减少了计算时间并最大程度地描述了图像底层信息。然后采用尺度不变特征变换(Scale-invariant feature transform, SIFT)描述符和统一模式的局部二值模式(Local binary pattern,LBP)描述符描述关键点周围的形状特征和纹理特征,引入K-Means聚类算法分别生成视觉词典,然后将局部描述符进行近似局部约束线性编码,并进行最大值特征汇聚。分别采用空间金字塔匹配生成具有空间信息的直方图,最后将金字塔直方图相串联,形成特征的图像级融合,并送入SVM进行分类识别。在公共数据库中进行实验,实验结果表明,本文所提方法能取得较高的目标识别准确率。     

Invariant texture classification using a spatial filter bank in multi-resolution analysis

This paper proposed a new method based on spatial filter banks and discrete wavelet transform (DWT) for invariant texture classification. The method used a multi-resolution analysis method like DWT and applied the proposed filter bank on different resolutions. Then, a simple fusion of features on different resolutions was used for invariant texture analysis. A comprehensive study was done to examine the effectiveness of the proposed method. Different datasets with different properties were used in this paper such as Brodatz, Outex, and KTH-TIPS for the evaluation. Local binary pattern (LBP) methods have been one of the powerful methods in recent years for invariant texture classification. A comparative study was performed with some state-of-the-art LBP methods. This comparison indicated promising results for the proposed approach as compared with the LBP methods.     

Image forgery detection using steerable pyramid transform and local binary pattern

In this paper, a novel image forgery detection method is proposed based on the steerable pyramid transform (SPT) and local binary pattern (LBP). First, given a color image, we transform it in the YCbCr color space and apply the SPT transform on chrominance channels Cb and Cr, yielding a number of multi-scale and multi-oriented subbands. Then, we describe the texture in each SPT subband using LBP histograms. The histograms from each subband are concatenated to produce a feature vector. Finally, a support vector machine uses the feature vector to classify images into forged or authentic. The proposed method has been evaluated on three publicly available image databases. Our experimental results demonstrate the effectiveness of the proposed method and its superiority over some recent other methods.     

基于局部二值模式与滤波器融合的人耳识别

人耳的丰富纹理信息为身份鉴别提供了重要解决途径。融合信号滤波变换与局部二值模式算子,构建了两种新型纹理描述算子,即局部Gabor二值模式算子与局部小波二值模式算子;提出了一套基于局部纹理特征描述算子的人耳识别方案。针对USTB人耳图像库三的测试实验表明,在选用Chi方相似性测度和局部Gabor二值模式算子时,1-NN分类器可获得98.14%的交叉验证识别率,且运行时间较少。     

纹理谱描述符及其在图像检索中的应用

为了提高纹理谱描述符的性能并降低其特征维数,在中心对称局部二值模式纹理谱描述符的基础上,提出一种融合局部区域中心像素以及灰度均值的改进纹理描述模式.首先根据图像局部区域内中心像素与其邻域像素间的灰度变化关系,定义了新的局部纹理模式;然后通过比较局部区域内灰度均值与图像全局灰度均值的大小,对局部纹理模式进行了增强处理.采用不同纹理图像库及不同的性能评价准则进行实验的结果表明,文中方法在基于内容图像检索中取得了较好的效果.     

基于纹理特征和轮廓光流矢量的烟雾识别

为了提高火灾探测的准确率和快速性,提出了基于纹理特征和轮廓光流矢量的烟雾识别算法。一方面为了获得更全面的纹理特征,建立图像金字塔,使用局部二值模式（ LBP）和基于方差的局部二值模式（ LBPV）结合的新方法分别提取金字塔不同层的纹理特征。另一方面是动态纹理特征,由于烟雾运动的湍流特性导致方向具有特定的一致性,改进了对全部可疑区域进行分析的方法,仅对可疑区域轮廓进行光流矢量分析,降低运算量。将静态纹理特征和动态纹理特征输入支持向量机（ SVM ）中进行识别。采用“静—静—动”的新型识别方法,实验结果表明：该算法能够及时准确报警,可靠率高。     

增强成对旋转不变的共生扩展局部二值模式

目的 针对成对旋转不变的共生局部二值模式（PRICoLBP）算法对图像光照、旋转变化鲁棒性较差,且存在特征维度过大的问题,提出了一种可融合多种局部纹理结构信息的有效特征——增强成对旋转不变的共生扩展局部二值模式。方法 首先,对图像各像素点的邻域像素点灰度值进行二值量化得到二值编码序列,并不断旋转二值序列得到以不同邻域点作为编码起始点对应的LBP值;然后,分别利用极大、极小LBP值对应的邻域起始编码点和中心像素点确定两个方向矢量,并沿这两个方向矢量在两个不同尺度图像上选取上下文共生点;其次,利用扩展局部二值模式（ELBP）算法的旋转不变均匀描述子来提取上下文共生点对的中心像素灰度级、邻域像素灰度级及径向灰度差异特征间的相关性信息;最后,用上下文共生点对的特征直方图训练卡方核支持向量机,检测纹理图像类别。结果 通过对Brodatz、Outex （TC10、TC12）、Outex （TC14）、CUReT、KTH-TIPS和UIUC纹理库的分类实验,改进算法的识别率比原始的PRICoLBP算法识别率分别提高了0.32%、0.57%、5.62%、3.34%、2.1%、4.75%。结论 利用像素点LBP特征极值对应的起始编码序列来选取上下共生点对,并用ELBP算法提取共生点对局部纹理信息,故本文方法能更好描述共生点对间的高阶曲率信息及更多局部纹理信息。在具光照、旋转变化的Outex、CUReT、KTH-TIPS纹理库图像分类实验中,所提方法比原始PRICoLBP算法取得了更高的识别率。实验结果表明,改进算法相比于原始算法能在较低的特征维度下对图像光照、旋转变化具有较好的鲁棒性。