基于小波的纹理特征提取.doc
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基于小波的纹理特征提取,摘 要纹理是人们视觉系统对自然界物体表面现象的一种感知，它作为物体表面的一种基本属性广泛存在于自然界中，是人们描述与区分不同物体的重要特征之一。纹理分析技术是图像处理领域一个经久不衰的研究热点，纹理特征提取作为纹理分析的首要问题，成为人们关注的焦点。本文在传统的纹理特征提取方法的基础上，提出了一种双树复小波域共生矩阵纹...
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摘 要
纹理是人们视觉系统对自然界物体表面现象的一种感知，它作为物体表面的一种基本属性广泛存在于自然界中，是人们描述与区分不同物体的重要特征之一。纹理分析技术是图像处理领域一个经久不衰的研究热点，纹理特征提取作为纹理分析的首要问题，成为人们关注的焦点。本文在传统的纹理特征提取方法的基础上，提出了一种双树复小波域共生矩阵纹理特征提取方法，对提取的特征值用聚类的方法进行性能分析，并应用于图像检索中。本文的主要工作如下：
1.复现了GLCM、DT-CWT纹理特征提取方法。通过实验比较了特征值之间的相关性，选择能量、熵、惯性矩和局部平稳这4个非相关特征值。构造共生矩阵参数，通过考察构造参数对特征值的影响来确定构造参数。该方法简洁、计算量小。在DWT和DT-CWT比较分析之后，利用DT-CWT从多方向和多尺度两个方面对图像纹理分析。设计了滤波器，并验证了DT-CWT的性质。
2.在深入研究GLCM和DT-CWT纹理特征提取方法的基础上，提出了一种双树复小波域的共生矩阵纹理特征提取方法。该方法利用双树复小波模型，构造同时满足正交和重构的滤波器，对纹理图像进行多层分解。通过计算多层低频子带图像的共生矩阵，提取描述纹理图像在不同尺度下的特征矢量；通过计算一层分解不同方向子带图像的共生矩阵，提取描述纹理图像不同方向的特征矢量。该方法能有效地描述纹理的尺度特性和方向特性，而且算法直观简明。
3.利用聚类分析，对GLCM、DT-CWT和双树复小波域共生矩阵的纹理特征提取方法所提取的特征矢量进行性能分析。将每一幅纹理图像所提取的特征矢量视为一个聚类的样本，不同类型所有纹理图像所提取的特征集合作为不同的聚类。通过聚类内部距离、聚类间距离及其比值等指标，分析比较了上述3种方法的特征提取性能。实验结果表明，本文提出的方法具有较好的聚类性能，所提取的纹理特征的聚类分离度优于其它两种方法，并能较好地保持聚类内部样本的差异性。
4.将GLCM、DT-CWT和双树复小波域共生矩阵的纹理特征提取方法应用于图像检索。利用检索图像与图像库中图像之间的纹理特征距离函数作为图像相似性度量值来检索图像，分析比较了上述3种方法的图像检索平均查准率。实验结果表明，本文提出的方法计算效率高、操作方便，有效地提高了图像检索的正确率。
关键词 纹理特征提取；双树复小波；灰度共生矩阵；聚类分析；图像检索
Abstract
Texture is a perception of the natural phenomenon from the visual system. It is widespread in nature as one of the basic properties of the surface, which always be used as the improtant characteristics to describe and distinguish the different objects. Texture analysis is a hotspot in image processing, since texture feature extraction is the primary problem of it, has been the focus of attention. A new method of texture feature extraction based on co-occurrence matrix in dual-tree complex wavelet domain was proposed in this paper. The paper analyzed the image texture features with clustering, and apply into image retrieva l. The main work and innovations are as follows:
1.The paper reproduce the texture feature extraction methods of GLCM and DT-CWT. In GLCM, it chooses energy, entropy, inertia and local stationary as the values of the texture feature. The structural parameters are determined by examing the impact of the texture feature. The experimental results showed GLCM is simple and less computation. In DT-CWT, by comprised the CWT and DT-CWT, we verify DT-CWT as the best way to analysis the images texture through the multi-scale and multi-directions.
2.A new method of texture feature extraction based on co-occurrence matrix in dual-tree complex wavelet domain was proposed in this paper. It uses dual-tree complex wavelet to decomposed the image texture with the filters which satisfy both orthogonal and reconstruction. The low-frequency sub-images produced by the multilayered dual-tree complex wavelet decomposition of texture images were utilized to calculate the co-occurrence in different directions for extracting the image texture features. The high-frequency sub-images produced by the multilayered dual-tree complex wavelet decomposition of texture images were utilized to calculate the co-occurrence in different directions for extracting the image texture features. The experimental results showed that this method can effectively extract the texture features in the multi-scale and multi-directions.
3.The paper uses clustering to do the performance analysis for the feature vectors which extract from the GLCM, DT-CWT and the new method. Then make each texture feature vectors as a sample of a cluster, all the different types of feature vectors form the cluster. By comparison the three methods of image texture extraction with internal distance, the distance between the cluster and the ratio of them, the experimental results show that the extracted texture features had favorable cluster separability and kept otherness of samples in the same cluster.
4.The paper apply the GLCM, DT-CWT and the new method into image retrieva l. Comparised three methods with the average precision of image retrieva l, the experimental results show that the new method has efficiency calculation, easy operation and improve the accuracy of image retrieva l effectivel..
纹理是人们视觉系统对自然界物体表面现象的一种感知，它作为物体表面的一种基本属性广泛存在于自然界中，是人们描述与区分不同物体的重要特征之一。纹理分析技术是图像处理领域一个经久不衰的研究热点，纹理特征提取作为纹理分析的首要问题，成为人们关注的焦点。本文在传统的纹理特征提取方法的基础上，提出了一种双树复小波域共生矩阵纹理特征提取方法，对提取的特征值用聚类的方法进行性能分析，并应用于图像检索中。本文的主要工作如下：
1.复现了GLCM、DT-CWT纹理特征提取方法。通过实验比较了特征值之间的相关性，选择能量、熵、惯性矩和局部平稳这4个非相关特征值。构造共生矩阵参数，通过考察构造参数对特征值的影响来确定构造参数。该方法简洁、计算量小。在DWT和DT-CWT比较分析之后，利用DT-CWT从多方向和多尺度两个方面对图像纹理分析。设计了滤波器，并验证了DT-CWT的性质。
2.在深入研究GLCM和DT-CWT纹理特征提取方法的基础上，提出了一种双树复小波域的共生矩阵纹理特征提取方法。该方法利用双树复小波模型，构造同时满足正交和重构的滤波器，对纹理图像进行多层分解。通过计算多层低频子带图像的共生矩阵，提取描述纹理图像在不同尺度下的特征矢量；通过计算一层分解不同方向子带图像的共生矩阵，提取描述纹理图像不同方向的特征矢量。该方法能有效地描述纹理的尺度特性和方向特性，而且算法直观简明。
3.利用聚类分析，对GLCM、DT-CWT和双树复小波域共生矩阵的纹理特征提取方法所提取的特征矢量进行性能分析。将每一幅纹理图像所提取的特征矢量视为一个聚类的样本，不同类型所有纹理图像所提取的特征集合作为不同的聚类。通过聚类内部距离、聚类间距离及其比值等指标，分析比较了上述3种方法的特征提取性能。实验结果表明，本文提出的方法具有较好的聚类性能，所提取的纹理特征的聚类分离度优于其它两种方法，并能较好地保持聚类内部样本的差异性。
4.将GLCM、DT-CWT和双树复小波域共生矩阵的纹理特征提取方法应用于图像检索。利用检索图像与图像库中图像之间的纹理特征距离函数作为图像相似性度量值来检索图像，分析比较了上述3种方法的图像检索平均查准率。实验结果表明，本文提出的方法计算效率高、操作方便，有效地提高了图像检索的正确率。
关键词 纹理特征提取；双树复小波；灰度共生矩阵；聚类分析；图像检索
Abstract
Texture is a perception of the natural phenomenon from the visual system. It is widespread in nature as one of the basic properties of the surface, which always be used as the improtant characteristics to describe and distinguish the different objects. Texture analysis is a hotspot in image processing, since texture feature extraction is the primary problem of it, has been the focus of attention. A new method of texture feature extraction based on co-occurrence matrix in dual-tree complex wavelet domain was proposed in this paper. The paper analyzed the image texture features with clustering, and apply into image retrieva l. The main work and innovations are as follows:
1.The paper reproduce the texture feature extraction methods of GLCM and DT-CWT. In GLCM, it chooses energy, entropy, inertia and local stationary as the values of the texture feature. The structural parameters are determined by examing the impact of the texture feature. The experimental results showed GLCM is simple and less computation. In DT-CWT, by comprised the CWT and DT-CWT, we verify DT-CWT as the best way to analysis the images texture through the multi-scale and multi-directions.
2.A new method of texture feature extraction based on co-occurrence matrix in dual-tree complex wavelet domain was proposed in this paper. It uses dual-tree complex wavelet to decomposed the image texture with the filters which satisfy both orthogonal and reconstruction. The low-frequency sub-images produced by the multilayered dual-tree complex wavelet decomposition of texture images were utilized to calculate the co-occurrence in different directions for extracting the image texture features. The high-frequency sub-images produced by the multilayered dual-tree complex wavelet decomposition of texture images were utilized to calculate the co-occurrence in different directions for extracting the image texture features. The experimental results showed that this method can effectively extract the texture features in the multi-scale and multi-directions.
3.The paper uses clustering to do the performance analysis for the feature vectors which extract from the GLCM, DT-CWT and the new method. Then make each texture feature vectors as a sample of a cluster, all the different types of feature vectors form the cluster. By comparison the three methods of image texture extraction with internal distance, the distance between the cluster and the ratio of them, the experimental results show that the extracted texture features had favorable cluster separability and kept otherness of samples in the same cluster.
4.The paper apply the GLCM, DT-CWT and the new method into image retrieva l. Comparised three methods with the average precision of image retrieva l, the experimental results show that the new method has efficiency calculation, easy operation and improve the accuracy of image retrieva l effectivel..
