Extraction of shift invariant wavelet features for classification of images with different sizes

An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.     

基于Log-Polar和DT-CWT的旋转不变纹理分类算法

提出了一种基于对数-极坐标变换和双树复数小波变换的旋转不变纹理分类算法。该方法首先对纹理图像进行对数-极坐标变换将旋转转化为平移,再用具有平移不变性的双树复数小波对变换后的图像滤波并计算各子带的能量值组成旋转不变特征向量,最后利用支持向量机算法实现纹理图像的分类。将本方法与其它旋转不变纹理分类算法进行比较,实验结果表明,提出的算法能有效地提高正确分类率。     

基于Radon变换和SWT的旋转不变纹理分类

提出了一种应用Radon变换和离散平稳小波变换(SWT)的旋转不变纹理分类算法。该方法首先对纹理图像进行Radon变换将旋转转化为平移,再用具有平移不变性的离散平稳小波对变换后的图像滤波并计算各子带的能量值组成旋转不变特征向量,最后利用支持向量机实现纹理图像的分类。将本方法与其它旋转不变纹理分类法进行比较,实验结果表明,提出的方法能有效地提高正确分类率。     

基于自相关图像的纹理特征检索的研究

针对图像检索时的平移、旋转及尺度变化问题,提出了一种基于自相关图像的不变性纹理特征提取方法.首先,用FFT、IFFT快速算法计算图像的自相关图像,消除平移影响,然后对自相关图像进行log-极坐标变化,这样就将旋转和尺度变化转为了平移,再用具有平移不变性的双树复小波进行分解,就可以提取出平移、旋转和尺度不变的特征向量.采用Canberra距离进行相似性度量.通过对发生几何变化的纹理图像库的实验表明,该方法对图像的平移、旋转和尺度变化具有较好的鲁棒性.     

基于DT-CWT和SVM的踏面旋转不变纹理提取算法

针对列车车轮踏面旋转纹理信息无法准确、有效提取的问题,提出一种基于Radon变换和双树复小波变换（DT-CWT）的列车车轮踏面特征提取方法。首先,对车轮踏面图像进行Radon变换;然后,对变换后的图像进行DT-CWT分解,使用分解后的各层低频子带系数和高频子带系数模的均值和标准方差构造特征向量,将其作为区分列车车轮踏面是否发生损伤的依据;最后,由支持向量机（SVM）进行分类决策。使用动车所采集的图像及人为加噪声后的图像进行分类实验,结果表明,本文使用的Radon和DT-CWT算法能有效地进行旋转不变纹理的提取,SVM分类正确率可以达到95%,可为列车车轮踏面状况检测提供更为准确便捷的方法支撑。     

Hierarchical kernel-based rotation and scale invariant similarity

Image similarity measure has been widely used in pattern recognition and computer vision. We usually face challenges in terms of rotation and scale changes. In order to overcome these problems, an effective similarity measure which is invariant to rotation and scale is proposed in this paper. Firstly, the proposed method applies the log-polar transform to eliminate the rotation and scale effect and produces a row and column translated log-polar image. Then the obtained log-polar image is passed to hierarchical kernels to eliminate the row and column translation effects. In this way, the output of the proposed method is invariant to rotation and scale. The theoretical analysis of invariance has also been given. In addition, an effective template sets construction method is presented to reduce computational complexity and to improve the accuracy of the proposed similarity measure. Through the experiments with several image data sets we demonstrate the advantages of the proposed approach: high classification accuracy and fast.     

A novel rotation/scale invariant template matching algorithm using weighted adaptive lifting scheme transform

This paper presents a novel algorithm for detecting user-selected objects in given test images based on a new adaptive lifting scheme transform. Given an object as a template, we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform based on the selected features. The goal of the new adaptive transform is to vanish the selected features in the transform domain. After applying both non-adaptive and adaptive transforms to a given test image, the corresponding transform domain coefficients are compared for detecting the object of interest. In addition, the proposed detection algorithm is combined with the proper log-polar mapping model in the parametric template space to attain rotation/scale invariance property. Finally, we have verified the properties of our proposed algorithm with experimental results.     

基于小波包变换和蚁群算法的纹理分类

提出了一种小波包变换和蚁群算法相结合的纹理分类新方法。首先采用小波包变换提取纹理图像的纹理特征向量,然后用蚁群算法进行训练和分类。实验表明小波包变换和蚁群算法应用到纹理分类领域,是一次有效的尝试。     

Affine invariant classification and retrieval of texture images

In this paper, we propose a new method of extracting affine invariant texture signatures for content-based affine invariant image retrieval (CBAIR). The algorithm discussed in this paper exploits the spectral signatures of texture images. Based on spectral representation of affine transform, anisotropic scale invariant signatures of orientation spectrum distributions are extracted. Peaks distribution vector (PDV) obtained from signature distributions captures texture properties invariant to affine transform. The PDV is used to measure the similarity between textures. Extensive experimental results are included to demonstrate the performance of the method in texture classification and CBAIR.     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

Rotation and gray scale transform invariant texture identificationusing wavelet decomposition and hidden Markov model

In this correspondence, we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter (QMF) bank and hidden Markov model (HMM). In the first stage, the QMF bank is used as the wavelet transform to decompose the texture image into subbands. The gray scale transform invariant features derived from the statistics based on first-order distribution of gray levels are then extracted from each subband image. In the second stage, the sequence of subbands is modeled as a hidden Markov model (HMM), and one HMM is designed for each class of textures. The HMM is used to exploit the dependence among these subbands, and is able to capture the trend of changes caused by rotation. During recognition, the unknown texture is matched against all the models. The best matched model identifies the texture class. Up to 93.33% classification accuracy is reported     

基于Radon变换的纹理图像多尺度不变量分析算法

为了更好地进行图像纹理分析,提出了一种基于Radon变换的不变量纹理识别算法。该算法首先利用Radon变换将图像投影到1维空间,然后通过对投影数据进行一种平移和比例不变的自适应小波变换来构造出具有比例和平移不变性的图像的特征矩阵。这种通过对特征矩阵进行多尺度分析得到的多尺度能量特征不但具有平移、比例和旋转不变性,而且反映出了纹理图像在不同尺度上的能量分布特征。在特征提取完成以后,即可利用支撑向量机进行分类。同其他方法的比较说明,该算法可较好地描述纹理特征,并可完成纹理识别。     

基于脊波变换的旋转不变性纹理特征提取方法

提出了一种新的基于脊波变换的旋转不变性纹理特征提取方法。该方法是先在脊波变换过程中的一维小波变换后所形成的每个频率子波段中提取特征,然后采用构建直方图的方法来提取同一尺度下高、低频子波段之间的关系特征,最后将这些特征进行一维傅里叶变换后取幅值并进行特征级融合,从而得到旋转不变性纹理特征。实验结果表明所提出的方法与两种已有的方法相比能够取得更好的分类效果。     

一种改进的综合纹理和形状特征的图像检索方法

提出一种利用小波进行综合纹理和形状特征的具有旋转、平移和尺度不变性的图像检索算法.使用角向矩加权方向定义图像的主方向来进行坐标轴的旋转矫正,得到图像的旋转不变性表示;采用具有平移和尺度不变性的小波变换对图像进行小波分解,利用各子带的能量作为纹理特征;利用小波分解的逼近子图重构图像并进一步利用Hu不变矩提取其形状特征.最后对纹理和形状特征进行高斯归一化,综合其特征进行检索.实验中对算法的尺度不变性、旋转不变性、平移不变性及对噪声的不敏感性进行了验证,实验结果证明了该算法具有更高的鲁棒性和查准率.     

Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system

Recently, significant of the robust texture image classification has increased. The texture image classification is used for many areas such as medicine image processing, radar image processing, etc. In this study, a new method for invariant pixel regions texture image classification is presented. Wavelet packet entropy adaptive network based fuzzy inference system (WPEANFIS) was developed for classification of the twenty 512 × 512 texture images obtained from Brodatz image album. There, sixty 32 × 32 image regions were randomly selected (overlapping or non-overlapping) from each of these 20 images. Thirty of these image regions and other 30 of these image regions are used for training and testing processing of the WPEANFIS, respectively. In this application study, Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively. In this way, effects to correct texture classification performance of these wavelet families were compared. Efficiency of WPEANFIS developed method was tested and a mean %93.12 recognition success was obtained.     

面向彩色图像的尺度和旋转不变性特征提取方法及应用

提出一种面向彩色图像的尺度和旋转不变性特征提取方法,并在真实的场景识别中进行了应用。该方法是先对给定彩色图像的各组成平面分别进行Radon变换,然后对得到的Radon变换系数矩阵进行尺度不变性处理,接着对处理后的Radon变换系数矩阵用频率B样条小波进行1维小波变换,在所得到的脊波系数矩阵中计算均值和方差的同时,采用线性回归模型提取在不同的颜色组成平面下所有频率子波段之间的关系属性,最后将得到的特征进行旋转不变性处理,从而得到所提出的尺度和旋转不变性特征。在3个数据库上进行了实验,结果表明本文方法可靠有效。     

结合微分特征和Haar小波分解的鲁棒纹理表达

针对传统的局部二值模式算子缺乏像素间深层次的相关性信息，且对图像中常见的模糊及旋转变化的鲁棒性较差的问题，提出了一种结合微分特征和Haar小波分解的鲁棒纹理表达算子。在微分特征通道上，通过各向同性的微分算子提取图像中的一阶和二阶微分特征，使图像的微分特征在本质上具有旋转不变性且对图像模糊具有较强的鲁棒性；基于小波变换在时域和频域同时具有良好的局部化的特点，在小波分解特征提取通道上采用多尺度的二维Haar小波分解提取图像中的模糊鲁棒特征；最后，串联两个通道上的特征直方图来描述图像的纹理特征。在特征判别性实验中，该算子在较复杂的UMD、UIUC和KTH-TIPS纹理库上的准确率分别达到了98.86%、98.2%和99.05%，与中值稳健扩展局部二值模式（MRELBP）算子相比，准确率分别提高了0.26%、1.32%和1.12%；在对旋转变化和图像模糊的鲁棒性分析实验中，该算子在仅存在旋转变化的TC10纹理库上的分类准确率达到99.87%，在添加了不同程度高斯模糊的TC11纹理库上的分类准确率降幅仅为6%；在计算复杂度实验中，该算子的特征维度仅为324维，在TC10纹理库上的平均特征提取时间为30.9 ms。实验结果表明，结合微分特征和Haar小波分解的方法具有很强的特征判别性，对旋转和模糊的鲁棒性较强，同时具有较低的计算复杂度，在样本数据较少的场合具有很好的适用性。     

结合微分特征和Haar小波分解的鲁棒纹理表达

针对传统的局部二值模式算子缺乏像素间深层次的相关性信息,且对图像中常见的模糊及旋转变化的鲁棒性较差的问题,提出了一种结合微分特征和Haar小波分解的鲁棒纹理表达算子。在微分特征通道上,通过各向同性的微分算子提取图像中的一阶和二阶微分特征,使图像的微分特征在本质上具有旋转不变性且对图像模糊具有较强的鲁棒性;基于小波变换在时域和频域同时具有良好的局部化的特点,在小波分解特征提取通道上采用多尺度的二维Haar小波分解提取图像中的模糊鲁棒特征;最后,串联两个通道上的特征直方图来描述图像的纹理特征。在特征判别性实验中,该算子在较复杂的UMD、UIUC和KTH-TIPS纹理库上的准确率分别达到了98.86%、98.2%和99.05%,与中值稳健扩展局部二值模式（MRELBP）算子相比,准确率分别提高了0.26%、1.32%和1.12%;在对旋转变化和图像模糊的鲁棒性分析实验中,该算子在仅存在旋转变化的TC10纹理库上的分类准确率达到99.87%,在添加了不同程度高斯模糊的TC11纹理库上的分类准确率降幅仅为6%;在计算复杂度实验中,该算子的特征维度仅为324维,在TC10纹理库上的平均特征提取时间为30.9 ms。实验结果表明,结合微分特征和Haar小波分解的方法具有很强的特征判别性,对旋转和模糊的鲁棒性较强,同时具有较低的计算复杂度,在样本数据较少的场合具有很好的适用性。     

基于多通道小波滤波的虹膜识别新算法

为克服小波变换过零检测虹膜识别算法对纹理灰度变化敏感的缺点,利用小波变换多通道滤波的特性,提出了一种新的基于Daubechies-4小波的虹膜识别新算法。根据虹膜纹理分布的特征,将虹膜分成10个分析带,对每个分析带采用一个合适尺度的小波滤波,小波变换各个通道的小波系数的均值及标准差作为虹膜的特征值,最后得到虹膜的128位特征编码。特征匹配采用的是加权欧式距离的分类器方法。实验结果表明算法是有效的,取得了高识别率;同时,提出的算法对虹膜图像的尺度、旋转、平移等的变化具有不变性。     

利用脉冲耦合神经网络的纹理图像检索方法

为提高基于内容的图像检索系统(CBIR)中纹理特征提取的有效性,进一步提升CBIR系统的整体性能。提出了一种基于脉冲耦合神经网络的纹理图像检索方法。脉冲耦合神经网络(PCNN)是新一代的人工神经网络,在数据处理上具有很多优势。特征提取时具有平移、旋转、尺度、扭曲等不变性,以及很好的抗噪性,而这一点非常适合于图像检索系统。利用PCNN及简化模型ICM得到对应于不同灰度值的二值图像序列,计算序列中每幅图像的熵序列,其一维的特征矢量作为纹理特征。采用Eu-clidean距离进行相似度计算,建立了一套基于示例查询图像的纹理图像检索系统。实验结果表明,与小波包等特征提取方法相比,该方法不仅对噪声具有较强的鲁棒性,同时能降低特征向量维数,具有尺度、平移和旋转不变性,而且能取得更高的检索率。