基于纹理的旋转不变图像检索算法的研究

提出一种基于Gabor变换的旋转不变多尺度广义粗糙度特征向量并结合自适应加权距离进行纹理图像检索的方法。利用图像Gabor分解的幅度谱,依据多尺度空间局部能量分布、Hurst分形指数、方向差别来计算纹理特征向量,最后采用自适应加权的街区距离作为相似性准则。仿真结果表明,该算法对旋转纹理图像取得了很好的检索结果。     

Rotation-invariant texture features from the steered Hermite transform

We propose the steered Hermite transform to analyze and capture visual patterns from textures regardless their orientation. Visual texture information is locally described as one dimensional patterns by steering the Cartesian Hermite coefficients according to the energy direction; therefore, no predefined orientation selective filters are required. We evaluate classification accuracy of some texture features individually. During the training stage, a filter selection strategy based on the augmented variance ratio analysis of the training features is employed in order to determine the filters that provide better classification accuracy and reduce computational costs during the classification stage.     

Rotation-invariant texture image retrieval using rotated complex wavelet filters.

This paper proposes a novel approach for rotation-invariant texture image retrieval by using set of dual-tree rotated complex wavelet filter (DT-RCWF) and DT complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. Two-dimensional RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Robust and efficient isotropic rotationally invariant features are extracted from DT-RCWF and DT-CWT decomposed subbands. This paper demonstrates the effectiveness of this new set of features on four different sets of rotated and nonrotated databases. Experimental results indicate that the proposed method improves retrieval accuracy from 83.17% to 93.71% on a small size (208 images) nonrotated database D1, from 82.71% to 90.86% on a small size (208 images) rotated database D2, from 72.18% to 76.09% on a medium-size (640 images) rotated database D3, and from 64.17% to 78.93% on a large size (1856 images) rotated database D4, compared with the discrete wavelet transform-based approach. New method also retains comparable levels of computational complexity.     

基于NSCT的旋转不变纹理图像检索算法

针对图像检索中常见的旋转问题,提出了一种基于非抽样Contourlet变换(NSCT)的旋转不变检索算法。以NSCT域各子带系数的均值和标准方差构成基本特征向量。在相同尺 度上,利用各子带的均值和标准方差之和对特征分量由小到大排序,同时根据旋转不变性调整排序后特征向量对应的方向序列,构造方向序列权值、特征分量权值。用加权欧氏距 离进行相似性度量以提高检索性能。采用Brodatz库生成实验库,实验结果表明本文方法取得了较好的检索效果。     

Rotation-invariant texture image retrieval using particle swarm optimization and support vector regression

This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subsequently, the PSO algorithm is utilized to optimize the RTIRPS method. Experimental results demonstrate that the RTIRPS method can achieve satisfying results and outperform the existing well-known rotation-invariant image retrieval methods under considerations here. Also, in order to reduce calculation complexity for image feature matching, the RTIRPS method employs the SVR to construct an efficient scheme for the image retrieval.     

Quaternionic wavelets for texture classification

This article proposes a study of the recent quaternionic wavelet transform (QWT) from a practical point of view through a digital image analysis application. Based on a theoretic 2D generalization of the analytic signal leading to a strong 2D signal modeling, this representation uses actual 2D analytic wavelets and yields subbands having a shift-invariant magnitude and a 3-angle phase, using the quaternion algebra.Our experiment furthers the understanding of this quite sophisticated tool, and shows its practical interest by a clear improvement of a famous wavelet application: texture classification. Thanks to coherent multiscale analysis brought by the QWT we obtain better classification results than with standard wavelets in a similar process.     

Unifying statistical texture classification frameworks

The objective of this paper is to examine statistical approaches to the classification of textured materials from a single image obtained under unknown viewpoint and illumination. The approaches investigated here are based on the joint probability distribution of filter responses.

We review previous work based on this formulation and make two observations. First, we show that there is a correspondence between the two common representations of filter outputs—textons and binned histograms. Second, we show that two classification methodologies, nearest neighbour matching and Bayesian classification, are equivalent for particular choices of the distance measure. We describe the pros and cons of these alternative representations and distance measures, and illustrate the discussion by classifying all the materials in the Columbia-Utrecht (CUReT) texture database.

These equivalences allow us to perform direct comparisons between the texton frequency matching framework, best exemplified by the classifiers of Leung and Malik [Int. J. Comput. Vis. 43 (2001) 29], Cula and Dana [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2001) 1041], and Varma and Zisserman [Proceedings of the Seventh European Conference on Computer Vision 3 (2002) 255], and the Bayesian framework most closely represented by the work of Konishi and Yuille [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2000) 125].     

Feature extraction for texture classification

We address the problem of texture classification. Random walks are simulated for plane domains $\text{A}$ bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification. Experiments using such a model have been performed and the results showed a rate of accuracy of 89.7% for a data set consisting of one hundred and twenty-eight textured images equally distributed among thirty-two classes of textures.     

Unsupervised classification of image texture

An automatic histogram-based algorithm for clustering statistical textural features of image incorporating estimation of the quality of the obtained distribution of feature vectors over clusters is presented. The algorithms is applied to classification of forest aerial imagery. Valeriya S. Sidorova. Born 1947. Graduated Novosibirsk State University in 1972. Researcher at Institute of Computational Mathematics and Mathematical Geophysics. Scientific interests: classification of remote sensing data, texture analysis, and 3D visualization. Author of 40 papers.     

Self-organizing maps for texture classification

A further investigation of our intelligent machine vision system for pattern recognition and texture image classification is discussed in this paper. A data set of 335 texture images is to be classified into several classes, based on their texture similarities, while no a priori human vision expert knowledge about the classes is available. Hence, unsupervised learning and self-organizing maps (SOM) neural networks are used for solving the classification problem. Nevertheless, in some of the experiments, a supervised texture analysis method is also considered for comparison purposes. Four major experiments are conducted: in the first one, classifiers are trained using all the extracted features without any statistical preprocessing; in the second simulation, the available features are normalized before being fed to a classifier; in the third experiment, the trained classifiers use linear transformations of the original features, received after preprocessing with principal component analysis; and in the last one, transforms of the features obtained after applying linear discriminant analysis are used. During the simulation, each test is performed 50 times implementing the proposed algorithm. Results from the employed unsupervised learning, after training, testing, and validation of the SOMs, are analyzed and critically compared with results from other authors.     

一种新的自然纹理分类方法

纹理分类一直是图像处理领域重要的研究课题之一。目前,用数学方法描述纹理特征从而进行纹理分类非常流行,但这些方法无法消除纹理视觉特征和人们理解的纹理概念之间的语义障碍。提出了一种新的基于中文自然语言纹理描述词的纹理方法,把常见的自然纹理分为10大类别,然后利用小波包分解和最小二乘支持向量机对自然纹理进行分类,实现了纹理的视觉特征到语义描述的转换。实验结果证明,该方法在图像理解和基于自然语言的图像检索中有助于缩小纹理特征的数学描述和人类理解之间的“语义鸿沟”。     

Multiple classifier system using classification confidence for texture classification

Multimedia Tools and Applications - This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is...     

Multiple resolution texture analysis and classification

Textures are classified based on the change in their properties with changing resolution. The area of the gray level surface is measured at serveral resolutions. This area decreases at coarser resolutions since fine details that contribute to the area disappear. Fractal properties of the picture are computed from the rate of this decrease in area, and are used for texture comparison and classification. The relation of a texture picture to its negative, and directional properties, are also discussed.     

Application-independent feature selection for texture classification

Recent developments in texture classification have shown that the proper integration of texture methods from different families leads to significant improvements in terms of classification rate compared to the use of a single family of texture methods. In order to reduce the computational burden of that integration process, a selection stage is necessary. In general, a large number of feature selection techniques have been proposed. However, a specific texture feature selection must be typically applied given a particular set of texture patterns to be classified. This paper describes a new texture feature selection algorithm that is independent of specific classification problems/applications and thus must only be run once given a set of available texture methods. The proposed application-independent selection scheme has been evaluated and compared to previous proposals on both Brodatz compositions and complex real images.     

Support vector machines for texture classification

This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification.     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Scale-adaptive local binary pattern for texture classification

Multimedia Tools and Applications - Local binary pattern (LBP) has already been proved to be a powerful measure of image texture with fixed sampling scheme: all P neighbor pixels in a single-scale...     

Wavelet packet neural networks for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%.