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。     

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

A novel approach for content-based texture image retrieval system using fuzzy logic classifier is proposed in this paper. The novelty of this method is demonstrated by handling the complexity issues in texture image retrieval arising from rotation and scale variance. These issues are divided into four groups as non rotated non scaled, rotation invariant, scale invariant and scale and rotation invariant texture retrieval for retrieval performance analysis. Features of texture images are obtained using discrete wavelet transform based statistical features and gray level co-occurrence matrix based co-occurrence features. The fuzzy logic classifier is developed with Gaussian membership function with mean and standard deviations of the features. The retrieval performance improvement is carried out by considering various combinations of the features. The average retrieval rates for the four issues have been achieved at 99.40% with 40 features, 91% with 80 features, 65.2% with 40 features, and 63.4% with 65 features respectively. This method outperforms the existing methods in terms of average retrieval rate. The scale and rotation invariant texture retrieval is an incomparable work that has been demonstrated in the present paper.     

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.     

A hybrid probabilistic framework for content-based image retrieval with feature weighting

In this paper, we propose a probabilistic framework for efficient retrieval and indexing of image collections. This framework uncovers the hierarchical structure underlying the collection from image features based on a hybrid model that combines both generative and discriminative learning. We adopt the generalized Dirichlet mixture and maximum likelihood for the generative learning in order to estimate accurately the statistical model of the data. Then, the resulting model is refined by a new discriminative likelihood that enhances the power of relevant features. Consequently, this new model is suitable for modeling high-dimensional data described by both semantic and low-level (visual) features. The semantic features are defined according to a known ontology while visual features represent the visual appearance such as color, shape, and texture. For validation purposes, we propose a new visual feature which has nice invariance properties to image transformations. Experiments on the Microsoft's collection (MSRCID) show clearly the merits of our approach in both retrieval and indexing.     

一种快速旋转不变图像检索新方法*

提出了一种新型快速旋转不变图像检索新方法.该方法首先对图像进行傅里叶变换和功率谱分解,提取功率谱的扇形区域能量和环形区域能量参数,并将其均值和标准差作为图像纹理特征.然后,利用谱能量分布特征把纹理的主方向旋转到0°,提取旋转后图像的共生矩阵参数和小波分解各子带图像统计参数作为基本特征.利用所提出的特征提取方法在两组分别包含25类单色自然纹理的图像库上进行检索试验.结果表明,该方法获得了良好的检索效果.     

基于统计特征的DCT压缩域纹理图像检索方法

提出了一种基于离散余弦变换（Discrete Cosine Transfrom,DCT）的纹理图像的检索方法.该方法在DCT压缩域,通过直接对DCT系数计算,获得图像纹理的统计特征,并作为检索的依据.理论分析和实验结果都表明,该方法具有很好的检索准确率和效率,并且对于旋转具有不变性.     

Texture image retrieval using new rotated complex wavelet filters.

A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) are designed with complex wavelet filter coefficients, which gives texture information strongly oriented in six different directions (45 degrees apart from complex wavelet transform). The 2-D RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree-complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. The information provided by DT-RCWF complements the information generated by DT-CWT. Features are obtained by computing the energy and standard deviation on each subband of the decomposed image. To check the retrieval performance, texture database D1 of 1856 textures from Brodatz album and database D2 of 640 texture images from VisTex image database is created. Experimental results indicates that the proposed method improves retrieval rate from 69.61% to 77.75% on database D1, and from 64.83% to 82.81% on database D2, in comparing with traditional discrete wavelet transform based approach. The proposed method also retains comparable levels of computational complexity.     

基于改进双树复小波和灰度-梯度共生矩阵的纹理图像检索算法

针对双树复小波变换缺少不同尺度纹理的空间分布特征的缺陷,提出了一种改进双树复小波和灰度-梯度共生矩阵相融合的纹理图像检索新算法。首先,该算法将图像进行非均匀分块,并对分块的图像进行双树复小波变换,以此增加不同尺度下的空间信息;其次,利用灰度-梯度共生矩阵提取4个统计量特征;然后, 融合 两种方法提取的纹理特征以得到图像检索的纹理特征;最后,用Canberra距离进行相似性度量并输出图像检索的结果。实验结果表明,该方法对纹理图像有较好的检索效果。     

A Model-Based Method for Rotation Invariant Texture Classification

This paper presents a new model-based approach for texture classification which is rotation invariant, i.e., the recognition accuracy is not affected if the orientation of the test texture is different from the orientation of the training samples. The method uses three statistical features, two of which are obtained from a new parametric model of the image called a ``circular symmetric autoregressive model.' Two of the proposed features have physical interpretation in terms of the roughness and directionality of the texture. The results of several classification experiments on differently oriented samples of natural textures including both microtextures and macrotextures are presented.     

Content-based image retrieval approach for biometric security using colour,texture and shape features controlled by fuzzy heuristics

In this paper, we discuss a new content-based image retrieval approach for biometric security, which is based on colour, texture and shape features and controlled by fuzzy heuristics. The proposed approach is based on the three well-known algorithms: colour histogram, texture and moment invariants. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and similarity metrics based on Euclidean measure. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The evaluation of the proposed approach is carried out using the standard precision and recall measures, and the results are compared with the well-known existing approaches. We present results which show that our proposed approach performs better than these approaches.     

改进颜色和小波变换纹理的图像检索方法研究

针对传统图像检索无法体现对检索示例图像中多个不同对象的检索要求程度的问题,提出一种改进颜色特征和小波变换纹理特征的图像检索方法。首先提取出图像的多个感兴趣区域,由感兴趣的不同程度分别赋予不同大小的权值;然后提取颜色特征和纹理特征,分别用对应位置相似度计算、感兴趣区域与检索数据库中图像整体的相似度计算和整体检索示例图像与检索图像数据库中图像相似度计算三种不同方法计算出两幅图像的相似度,取最大的相似度作为两幅图像的最终相似度;对检索示例图像与检索数据库中每个图像的相似度按大小进行排序,选择最相似的图像作为检索结果。实验结果表明,该方法提高了对图像检索的性能,体现了个性化检索,对图像检索具有很好的效果。     

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.     

基于复数小波域广义高斯分布模型的

由于在频域用能量参数来表示图像的特征矢量缺乏准确性,而且实数离散小波变换具有平移变化性和弱的方向选择性,为此针对以上问题提出了一种基于复数小波域广义高斯分布模型的纹理图像检索方法。该方法首先利用双树复数小波变换系数的统计特性来建立广义高斯分布的统计模型;然后基于该模型提取图像的特征矢量;最后利用KullbackLeibler distance(KLD)测度算法进行纹理图像检索。对Brodatz图像库的仿真表明,新方法较双树复数小波算法的查准率提高6.96%,较基于Gabor纹理特征检索法的查准率提高了18.8%。同时复数小波系数统计模型具有旋转不变性。新方法对今后的纹理图像检索具有重要的理论与实际意义。     

基于内容的标准烟叶图像数据库检索

基于内容的检索是当前多媒体数据库发展的一个重要研究领域。在图像数据库中,基于内容的图像检索技术一般采用颜色直方图为特征。该文提出了把图像的形状特征、颜色特征和纹理特征结合起来的方法进行基于内容的图像检索。通过对人的视觉检索过程的研究,提出把数据库中的图像依次分别按形状特征、颜色特征和纹理特征分级聚类的方法,既符合人的视觉特点又大大提高了检索效率。     

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.     

基于复数小波域广义高斯分布模型的纹理图像检索

由于在频域用能量参数来表示图像的特征矢量缺乏准确性,而且实数离散小波变换具有平移变化性和弱的方向选择性,为此针对以上问题提出了一种基于复数小波域广义高斯分布模型的纹理图像检索方法。该方法首先利用双树复数小波变换系数的统计特性来建立广义高斯分布的统计模型;然后基于该模型提取图像的特征矢量;最后利用Kullback-Leibler distance(KLD)测度算法进行纹理图像检索。对Brodatz图像库的仿真表明,新方法较双树复数小波算法的查准率提高6.96%,较基于Gabor纹理特征检索法的查准率提高了18.8%。同时复数小波系数统计模型具有旋转不变性。新方法对今后的纹理图像检索具有重要的理论与实际意义。     

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。     

基于ColorMax模型的拟视皮层图像识别*

受生物学研究启发,模拟视皮层组织结构提出了ColorMax层次模型用于彩色图像识别问题。利用ColorMax模型进行学习能得到较高层次的复杂仿真视觉特征,这些特征具有较好的识别可分性和不变性。利用该模型实现基于对象颜色、纹理和形状的综合特征识别与比较。实验结果表明,提出的模型能够在学习样本数量少的情况下进行学习,提高了识别的速度,能达到与当前先进算法相当识别效果。