Efficient distance-based per-pixel texture classification with Gabor wavelet filters

This paper proposes an efficient solution to the problem of per-pixel classification of textured images with multichannel Gabor wavelet filters based on a selection scheme that automatically determines a subset of prototypes that characterize each texture class. Results with Brodatz compositions and outdoor images, and comparisons with alternative classification techniques are presented.     

Filter-based models for pattern classification

In this paper we consider a technique for pattern classification based upon the development of prototypes which capture the distinguishing features (“disjunctive prototypes”) of each pattern class and, via cross-correlation with incoming test images, enable efficient pattern classification. We evaluate such a classification procedure with prototypes based on the images per se (direct code), Gabor scheme (multiple fixed filter representation) and an edge (scale space-based) coding scheme. Our analyses, and comparisons with human pattern classification performance, indicate that the edge-only disjunctive prototypes provide the most discriminating classification performance and are the more representative of human behaviour.     

A new benchmark image test suite for evaluating colour texture classification schemes

Several image test suites are available in the literature to evaluate the performance of classification schemes. In the framework of colour texture classification, OuTex-TC-00013 (OuTex) and Contrib-TC-00006 (VisTex) are often used. These colour texture image sets have allowed the accuracies reached by many classification schemes to be compared. However, by analysing the classification results obtained with these two sets of colour texture images, we have noticed that the use of colour histogram yields a higher rate of well-classified images compared to colour texture features. It does not take into account any texture information in the image, this incoherence leads us to question the relevance of these two benchmark colour texture sets for measuring the performances of colour texture classification algorithms. Indeed, the partitioning used to build these two sets consists of extracting training and validating sub-images of an original image. We show that such partitioning leads to biased classification results when it is combined with a classifier such as the nearest neighbour. In this paper a new relevant image test suite is proposed for evaluating colour texture classification schemes. The training and the validating sub-images come from different original images in order to ensure that the correlation of the colour texture images is minimized.     

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。     

A multi-scale supervised orientational invariant neural architecture for natural texture classification

A multi-scale supervised neural architecture, called Multi-Scale SOON, is proposed for natural texture classification. This architecture recognizes the input textured image through a hierarchical categorization structure in multiple scales. This process consists of three sequential phases: a multi-scale feature extraction, a scale prototype pattern generation, and a multi-scale prototype fusion pattern classification. First phase extracts scale textural features using the Gabor filtering. Then, a hierarchical categorization shapes the classification. First categorization level generates the scale prototypes and an upper level categorizes the prototypes fusion. Three increasing complexity tests over the well-known Brodatz database are performed in order to quantify the Multi-Scale SOON behavior. The comparison to other standout methods proves Multi-Scale SOON behavior to be satisfactory. The tests, including the entire texture album, show the stability and robustness of the Multi-Scale SOON response.     

Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images

This paper describes a method of improving classification accuracy when using Synthetic Aperture Radar (SAR) images. The classifier used is a maximum likelihood classifier. Texture and textural feature images were made and used for classification. The accuracy of various classification methods was compared. As a result, it was found that the best classification was produced by the aggregation of the classified image when using texture images as additional inputs to the classifier. It is also shown that textural analysis and the aggregation technique are useful in the classification of SAR images.     

Segmentation of remotely-sensed images by a split-and-merge process+

Abstract

This paper describes the application of an image segmentation technique to remotely-sensed terrain images used for environmental monitoring. The segmentation is a preprocessing operation which is applied prior to image classification in order to improve classification accuracy from that achievable by classifying pixels individually on the basis of their spectral signatures. The method uses a split-and-merge technique to segment images into regions of homogeneous tone and texture wherever this is possible. The split-and-merge technique employs a hierarchical quadtree data structure. Texture is measured using easily computed grey value difference statistics. The homogeneity criteria employed in region merging are dependent on local statistics. The segmented image is classified using a region classifier for regions and the normal per-pixel classifier for single pixels in areas of inhomogeneity. The technique is illustrated by example classifications of aerial Multispectral Scanner data from two test sites. A quantitative analysis of the performance shows that an increased classification accuracy is achieved.     

Hyperspectral data spectrum and texture band selection based on the subspace-rough set method

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images.     

面向照片-图形图像的二分类方法

随着科技的发展,图像应用技术日趋重要,许多图像应用技术对照片图像和图形图像的要求和效果是不同的.针对照片图像和图形图像分类的问题,本文提出了一种新的基于二分法的图像分类方法,这种方法混合了颜色、边缘和纹理三种图像特征,通过对图像特征值的K-means聚类分析,实现了照片图像和图形图像的分类.经实验验证,该方法在照片图像和图形图像的分类上取得较好的结果.     

基于地质统计学纹理的遥感影像分类研究进展

传统的基于像素与像素基础上的遥感影像光谱分类方法忽视了邻近像素值之间潜在有用的空间信息,三十多年来,人们一直都在谋求利用遥感影像本身所固有的空间信息以加强光谱分类,尽管从事该方面研究的人一直都很少,其实现的手段主要依靠对原始影像的滤波,滤波的一般方式是生成纹理波段以指导接下来的分类。近年来,变异函数被用来表达空间依赖性,并取代简单的方差滤波成为了纹理分类的主要手段,在这篇综述性的论文中,笔者主要讨论了两类将基于地质统计学的纹理信息集成到遥感影像分类中的应用,它们代表了当前遥感影像纹理分类的主流。     

Efficient median based clustering and classification techniques for protein sequences

In this paper, an efficient K-medians clustering (unsupervised) algorithm for prototype selection and Supervised K-medians (SKM) classification technique for protein sequences are presented. For sequence data sets, a median string/sequence can be used as the cluster/group representative. In K-medians clustering technique, a desired number of clusters, K, each represented by a median string/sequence, is generated and these median sequences are used as prototypes for classifying the new/test sequence whereas in SKM classification technique, median sequence in each group/class of labelled protein sequences is determined and the set of median sequences is used as prototypes for classification purpose. It is found that the K-medians clustering technique outperforms the leader based technique and also SKM classification technique performs better than that of motifs based approach for the data sets used. We further use a simple technique to reduce time and space requirements during protein sequence clustering and classification. During training and testing phase, the similarity score value between a pair of sequences is determined by selecting a portion of the sequence instead of the entire sequence. It is like selecting a subset of features for sequence data sets. The experimental results of the proposed method on K-medians, SKM and Nearest Neighbour Classifier (NNC) techniques show that the Classification Accuracy (CA) using the prototypes generated/used does not degrade much but the training and testing time are reduced significantly. Thus the experimental results indicate that the similarity score does not need to be calculated by considering the entire length of the sequence for achieving a good CA. Even space requirement is reduced during both training and classification.     

Using association rules as texture features

A new type of texture feature based on association rules is proposed in this paper. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. Association rules capture both structural and statistical information, and automatically identifies the structures that occur most frequently and relationships that have significant discriminative power. Methods for classification and segmentation of textured images using association rules as texture features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. It is shown that association rule features can distinguish texture pairs with identical first, second, and third order statistics, and texture pairs that are not easily discriminable visually     

A spectral–textural kernel-based classification method of remotely sensed images

Most studies have been based on the original computation mode of semivariogram and discrete semivariance values. In this paper, a set of texture features are described to improve the accuracy of object-oriented classification in remotely sensed images. So, we proposed a classification method support vector machine (SVM) with spectral information and texture features (ST-SVM), which incorporates texture features in remotely sensed images into SVM. Using kernel methods, the spectral information and texture features are jointly used for the classification by a SVM formulation. Then, the texture features were calculated based on segmented block matrix image objects using the panchromatic band. A comparison of classification results on real-world data sets demonstrates that the texture features in this paper are useful supplement information for the spectral object-oriented classification, and proposed ST-SVM classification accuracy than the traditional SVM method with only spectral information.     

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets.     

基于角度径向变换的旋转不变纹理分类

纹理分类广泛的应用于医学图像分析等领域,纹理图像的采集因拍摄角度的变化产生一定的旋转,本文提出一种基于角度径向变换的旋转不变纹理分类方法。首先采用角度径向变换方法对图像进行特征提取,分别得到图像的角向特征向量和径向特征向量;然后将提取出的2组特征向量结合起来作为图像的整体特征向量,利用K近邻特征空间距离的分类方法进行纹理分类。选取Brodatz纹理库中的图像进行纹理分类测试,实验结果表明,该算法具有较好的旋转不变纹理分类效果。     

基于扩展CENTRIST的遥感场景分类

提出一种基于扩展CENTRIST纹理算子的遥感场景分类方法。它由更多邻域规模的三个子方案组成,不仅继承了CENTRIST的优点,而且编码了更多不同纹理的局部结构信息。通过三种不同模式的纹理算子来提取多通道图像纹理特征,通过谱回归判别分析进行分类识别。提出能够捕获多通道图像中互补信息的多通道eCT融合机制,以获得更高的分类准确率。在UC Merced标准数据库上的实验表明,该方法得到的结果比CENTRIST效果更好,鲁棒性更高。     

GLCM和DWT特征在打印文件机源认证中的应用

为了有效解决打印文件机源认证问题,提出了一种基于统计纹理特征选择的打印文件机源认证方法。综合考虑打印字符图像的空间域和时频域特性,将GLCM和DWT统计纹理特征进行组合,运用ReliefF算法实现组合特征的初选,二次特征选择使用SVM-RFE算法。文中实验结果表明,在英文相同字有重复样本集和中文不同字无重复样本集上的分类准确率分别为95.20%和75.00%;特征组合与特征选择有利于提高打印文件机源认证的分类鉴别性能。     

Texture measures combination for improved meningioma classification of histopathological images

Providing an improved technique which can assist pathologists in correctly classifying meningioma tumours with a significant accuracy is our main objective. The proposed technique, which is based on optimum texture measure combination, inspects the separability of the RGB colour channels and selects the channel which best segments the cell nuclei of the histopathological images. The morphological gradient was applied to extract the region of interest for each subtype and for elimination of possible noise (e.g. cracks) which might occur during biopsy preparation. Meningioma texture features are extracted by four different texture measures (two model-based and two statistical-based) and then corresponding features are fused together in different combinations after excluding highly correlated features, and a Bayesian classifier was used for meningioma subtype discrimination. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations in terms of quantitatively characterising the meningioma tissue, achieving an overall classification accuracy of 92.50%, improving from 83.75% which is the best accuracy achieved if the texture measures are used individually.