线状纹理的一种属性关系图描述方法及其应用

纹理特征是图像分析的重要线索，纹理分析方法可以分为统计方法和结构方法，两者各有优劣，结合统计分析方法和结构分析方法两个方面的优点，提出了一种线状纹理的属性关系图（attributed relational graphs,ARG）描述方法，用属性关系图来描述图像纹理，并应用于图像检索中，属性关系图描述方法用图的结构直观地描述了图像的特征，具有很强的表达能力，提取直线段作为线状纹理的基元，并引入了基元间具有平移、旋转和伸缩不变性的关系属性，使纹理图像的识别和检索具有好的抗噪声能力，实验证明该方法取得了令人满意的识别和检索效果。     

基于统计地形特征的纹理检索方法

提出了一种利用图像函数图的信息来进行纹理描述的方法.图像函数图在三维空间中所形成的曲面形状看起来像地形地貌.提出的方法被称为统计地形特征,它首先用一个可变水平平面去切割图像函数的图可得到一些实体,然后从这些实体的几何和拓扑属性推导出4条特征曲线来描述纹理特征.在Brodatz和VisTex纹理集上的比较实验结果表明,统计地形特征的检索性能高于多维度自回归纹理模型、统计几何特征和离散小波变换等方法.     

基于纹理的乳房超声图像中肿瘤区域自动检测

纹理属性是医学图像分析的一个非常重要的方法,探索利用一种称为统计地形特征的新的纹理描述方法自动监测超声医学图像乳房肿瘤区域。统计地形特征首先用一个可变水平平面去切割图像函数图可以得到一些实体,然后利用从这些实体的几何和拓扑属性的某些统计信息提取的6条特征曲线来描述纹理。文中实验部分验证了统计地形特征在自动监测超声医学图像乳房肿瘤区域中具有良好的效果。     

基于多纹理特征的商标图像检索

提出了一种基于多纹理特征的商标图像检索方法。首先对图像纹理特征进行分析，从人眼视觉角度选用粗糙度和方向性这2个纹理特征量；从统计分析的角度出发，基于图像灰度共生矩阵描述了二阶矩、熵、对比度和均匀性这4个纹理特征量。这6个纹理特征量从不同角度刻画了图像特征，对其归一化后用欧氏距离进行图像相似性度量。通过实验，证明了该方法可以取得比较满意的检索结果。     

一种基于分形维数的图像相似性检索特征

本文描述了一种采用小波包分解子带图像分形维数作为图像纹理特征进行图像相似性检索的方法。首先对图像进行小波包分解。每一个子带图像的分形维数构成图像的特征向量用于图像的相似性检索。实验表明,该方法计算速度快。在特征向量维数极少的情况下,该特征对于描述方向性和结构性较好的纹理具有良好效果。     

一种快速有效的图像纹理谱描述子

提出了一种基于纹理基元等价类的纹理谱描述子来描述图像的纹理特征．该纹理谱刻画邻域内像素灰度变化模式，以纹理谱直方图方式表示图像内容．与Gabor纹理特征的对比实验表明，文中的纹理谱描述子特征提取速度快、检索准确率高．最后给出了实验结果和性能评价．     

基于多邻域空间分布的图像检索

由于灰度共生矩阵及其改进算法存在计算复杂、且对于纹理分布信息缺乏计算以及忽略了图像相关特性等缺点,导致对于图像纹理的有效信息缺乏很好的描述,为此提出了一种新的纹理特征用于图像检索.该算法首先结合图像中像素的统计信息,针对不同的邻域范围提取图像的邻域相关矩阵,然后在此基础上构造多邻域空间分布特征用于图像检索.分析表明,该算法所提取的纹理特征计算量小,复杂度低,并且由于将纹理的结构特征和统计特征有效地结合起来,所以对图像的空间纹理分布特征可以较好地描述.为了证明新算法所提取纹理特征的有效性,将其用于图像检索实验.实验结果表明,新算法在检索精度上相比其他算法具有较大的提高.     

MPEG-7纹理描述子的图像检索

利用MPEG-7中所制订的统一格式,来提取纹理图像的特征,其中使用了同构型纹理图像描述子、纹理图像浏览描述子和边界直方图描述子三种特征描述来检索图像。同构型纹理图像描述子是使用Gabor滤波器来加强特定纹理方向和纹理大小的信号,并计算在各个频道的能量强度。纹理图像浏览描述子也是利用Gabor滤波器来提取纹理的方向性,并利用找出的方向性经过投影和自相关函数来找出纹理大小,并判断规则度。而边界直方图描述子则是找出在图像区块中的边界型态,统计成直方图来作为特征。文中使用了上述三种描述子,实验结果显示可以检索出最相似的纹理图像,但是每种描述子仍有它使用的限制和缺点。同构型纹理图像描述子适合使用在同构型较高的纹理图像上;纹理图像浏览描述子对于非30倍数的角度较不敏感,容易出现误差;边界直方图描述子只适用于有明显边界分布出现的图像。     

一种新的自适应特征谱

图像特征谱给予图像原始特征以富有表达力的、统一的、可操作的形式化描述。本文针对单一图像特征谱在应用时对图像信息表达的片面性和干扰性问题,提出了一种自适应特征谱,该特征谱利用纹理谱对纹理特征的描述将图像区域划分为光滑区和纹理区,对于光滑区域将选择颜色谱作为区域特征,而对于纹理区将选择纹理谱作为特征值,因此它可以对具有不同视觉特点的图像给予灵活的特征描述。为了检验该特征谱的有效性,我们将它应用于基于内容的图像检索中,实验结果表明,利用所提出的自适应特征谱具有很好的检索效果和较小的时间复杂度。     

基于纹理谱描述子的图像检索

提出一种新的纹理谱描述子应用于基于内容的图像检索中.讨论了小波变换的思想和纹理谱概念的联系,根据纹理的视觉特性,提出纹理模式等价类的概念,设计出更合理的纹理谱描述子来描述图像的纹理特征.该纹理模式刻画了领域内像素灰度变化模式,以纹理谱直方图方式表示图像纹理内容.分析了纹理谱的对称不变性和旋转鲁棒性的特点.应用于图像检索,与G曲or纹理特征相比较,该纹理谱描述子特征提取速度快,检索准确率高.     

一种融合颜色和纹理特征的遥感图像检索方法

海量遥感图像的自动查询和选择，迫切需要有效的基于内容的图像检索方法。鉴于单一视觉特征不能很好地表达图像内容，为此提出一种基于五叉树分解的线性加权颜色和纹理特征距离的检索新方法。该方法首先采用五叉树分解法分解图像，然后在利用多通道Gabor滤波器与图像做卷积得到滤波能量值的基础上，提取各子图像滤波能量纹理特征，最后通过计算子图像的颜色均值和均方差来对查询图像和与其大小相当的数据库子图像进行颜色和纹理特征线性加权距离相似性测度。将该方法用于高分辨率卫星和航空遥感图像数据库检索的实验结果证明，该方法是有效的。     

Predicting old-growth tropical forest attributes from very high resolution (VHR)-derived surface metrics

Old-growth tropical forests are increasingly vanishing worldwide. Although the accurate quantification of tropical old-growth forests attributes is essential to understand, manage, and conserve their high diversity and biomass, conducting this task over large areas and at fine detail is not only expensive and time consuming, but also often practically impossible. This calls for the search for more efficient alternatives, particularly those based on remote sensing. In this study, we evaluate the potential of several surface metrics (tone and texture) extracted from very high resolution (VHR) satellite imagery to model the structural and diversity attributes of a tropical dry forest (TDF) in southern Mexico. We constructed simple linear models that used each forest attribute as dependent variables, and the tone and texture metrics extracted from several bands, the panchromatic (resolution = 0.5 m), red (R), infrared, and two vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI); resolution = 2 m), of a VHR image (GeoEye-1) as predictive variables. The significance of the models including one, two, two and its interaction, and three image metrics was evaluated by comparing them with null models. The structural characteristics of the TDF (basal area (BA), mean height, stem density) showed the highest modelling potential, with the goodness-of-fit (R²) values ranging from 0.58 to 0.66. Conversely, no significant models were obtained for total crown area (TCA) and all diversity attributes. Our results show that remote-sensing metrics detect the spatial variation in the structural attributes of this old-growth TDF better than they detect the variation in its diversity. Our ability to model forest attributes at large scales at fine detail (sampling plots <0.2 ha) can be much improved by combining the use of VHR imagery with an array as wide as possible of the image surface metrics, including both tone and texture.     

基于兴趣点的图像检索

在基于内容的图像检索中,往往使用颜色、纹理以及形状的全局特征来描述图像,然而全局特征不能描述图像的细节,丢失了图像的空间信息。文章利用兴趣点来灵活描述图像的局部信息,提取兴趣点周围的颜色矩作为局部特征,通过兴趣点的匹配和带权投票来进行相似度量,几何哈希技术的使用增强了兴趣点间的正确匹配。实验证明了这种方法的有效性,具有旋转、平移和部分的尺度不变性。     

一种新的基于分块颜色和形状的图像检索方法

基于图像单一特征的检索方法只能反映图像的部分属性，应用范围受到限制。本文在分析图像颜色和形状特征的基础上，根据各自的特点，提出了一种新的基于分块颜色和形状的图像检索方法。首先，在图像分块的基础上提取颜色和特征点；其次，根据特征点提取图像的形状信息；最后，以颜色和形状作为综合特征，采用相应的匹配算法进行图像检索。实验证明该方法不仅准确度高，而且还可应用于基于对象或区域的图像查询中。     

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

基于二进小波变换及局部二值模式特征的图像检索

纹理是一种反映像素的空间分布属性的图像特征,成为图像分析的重要手段。在图像检索领域,由于小波变换具有方向性、局部特性、多尺度特性,使得图像的纹理特征能得到更好的表征。局部二值模式是提取图像纹理特征的有效工具,具有旋转不变性和灰度不变性。利用性能良好的 Bubble 小波对图像进行二进小波变换得到四个子带图像,其中低频子带包含原图信息最多,对角线方向的子图包含信息最少,其余两个子图包含信息介于两者之间,因此选取低频和垂直,水平高频图像,然后对分量利用等价局部二值模式提取图像纹理特征,并进行图像检索。实验结果表明该算法不仅有效地结合了小波与局部二值模式的优势,而且具有较好的检索性能。     

A novel retrieval method for remote sensing image based on statistical model

With the increasing number of high-resolution remote sensing (HRRS) image technologies, there is an interest in seeking a way to retrieve images efficiently. In order to describe the images with abundant texture information more concisely and accurately, we propose a novel remote sensing image retrieval approach based on the statistical features of non-subsampled shearlet transform (NSST) coefficients, according to which we set up a model using Bessel K form (BKF). First, the remote sensing (RS) image is decomposed into several subbands of frequency and orientation using the non-subsampled shearlet transform. Then, we use the Bessel K distribution model is utilized to describe the coefficients of NSST high-frequency subband. Next, the BKF parameters are selected to serve as the texture feature to represent the characteristics of image, namely BKF statistical model feature (BSMF), and the feature vector of each image is created by combination with parameters at each high-pass subband. Both the experiment and theory indicate that the BKF distribution is highly matched with the statistical features of NSST coefficients within high-pass subbands. In our experiments, we applied the proposed method to two general RS image datasets- The UC Merced land use dataset and the Sydney dataset. The results show that our proposed method can achieve a more robust and commendable performance than the state-of-the-art approaches.     

Bayesian texture classification and retrieval based on multiscale feature vector

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.