利用模糊认知度从图像纹理中提取情感语义

图像的低层视觉特征（颜色、纹理和形状等）中包含着大量人类可感知的情感语义信息。利用纹理特征,提出一种新的索引方法-FRD（Fuzzy Recognize Degree,模糊认识度）聚类法,用来描述与情感相关联的语义图像。FRD聚类法使用三个感性的纹理特征：方向性、对比度和粗糙度,生成FRD值。从高层的情感概念进行图像检索。实验图像采用自然景观图片,验证了新方法的良好性能。     

基于多模态特征的医学图像聚类方法

现有的图像特征表达大多使用低层语义特征(如颜色、纹理等)细粒度地比较图像的相似度,然而医生就诊更多依据图像在局部区域高层语义特征(如是否病变、病变类型等)的差异粗粒度地判断图像的相似程度。针对现有的医学图像特征表达忽略了医学图像特有的高层语义特征,致使医学图像聚类效果不佳的问题,提出了一种融合医学图像纹理特征和特有形态学特征的多模态特征医学图像聚类方法。首先一方面提出使用纹理特征融合方法表示医学图像全局底层语义特征;另一方面提出使用图像分割的感兴趣区域(region of interest,ROI)的形态学描述作为形态学特征表示医学图像的局部高层语义信息。其次结合提出的相似性度量方法分别计算脑CT图像两类特征间的相似度。最后利用多核学习方法学习特征融合权重,并在多核谱聚类实验上验证了该方法的有效性。     

基于两种纹理特征聚类的图像检索

设计和实现两种不同的分形维数作为纹理特征进行聚类的方法.提取几百幅不同图像的两种纹理特征,对特征库按聚类算法建立索引结构,形成图像的分类库,通过两种不同纹理特征的检索与无聚类的特征检索相比,实验结果表明聚类方法大大缩短了检索时间.结论:作为海量图像的检索,有效的图像特征结合聚类是一个有力工具,在研究信息分类与识别等方面具有应用潜力.     

低层特征获取高层语义的图像检索

首先采用基于颜色聚类的方法将图像分割成区域,提取每个区域的Gabor小波纹理特征和灰度共生矩阵纹理特征,接着采用信息熵对特征进行选择,使用选择后的特征对图像区域进行聚类,得到每幅图像的语义特征向量;然后提出遗传模糊C均值算法对图像进行聚类。在图像检索时,查询图像和聚类中心比较,在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,提高了检索的精度。     

基于遗传算法和 FCM 的图像自动标注*

为了缩减图像底层视觉特征与高层语义之间的“语义鸿沟”及减少聚类的不稳定性,论文提出了一种基于遗传算法和FCM的图像自动标注方法。该方法首先提取图像的颜色和纹理特征,然后运用遗传算法和FCM 相结合的方法对图像进行聚类。最后通过支持向量机学习训练库的图像特征构造简单的多类支持向量机模型实现图像的自动标注。实验表明,该方法具有很好的图像标注性能。     

SVM用于基于块划分特征提取的图像分类

在基于内容图像检索中,图像的底层视觉特征和高层语义概念之间存在着较大的语义间隔。使用机器学习方法学习图像特征,自动建立图像类的模型成为一种有效的方法。本文提出了一种用支持向量机(SVM)实现自然图像自动语义归类的方法,基于块划分聚类得到特征向量作为SVM训练样本,实现语义分类器。由于参与聚类的是某类图像所有块的特征,提取的特征更能反映某一类图像特征。实验证明这种方法是有效的。     

基于语义分类的外观专利图像快速检索系统

针对大规模专利图像特征库的特点,使用边缘轮廓距离与分块特征相结合的方法提取低层视觉特征,结合基于K均值聚类的分类索引方法,兼顾语义相似和视觉特征相似,对专利图像库数据构建索引结构,实现了先分类后检索的功能。实验结果表明,方法不仅提高了检索速度,而且提高了检索的语义敏感度。     

基于多特征融合的抽象画情感研究

抽象画作为一种寓意含蓄的艺术作品,传递出的情感也是含蓄的,确定其情感分类也比较困难,为此采用多特征融合方式预测抽象画的情感。首先采用K-means聚类提取抽象画图像的主色调作为底层颜色特征,采用灰度—梯度共生矩阵提取底层纹理特征,采用卷积神经网络自动提取高层语义特征;其次由于特征维度不同,采用多核学习对底层和高级语义特征进行融合;最后采用支持向量机实现抽象画情感识别,分为积极与消极两类。在MART数据集上进行测试,并与其他现有分类模型进行了比较,实验结果显示该方法在测试性能上优于已有模型。     

一种结合语义特征和视觉特征的图像检索方法

为了解决传统的CBIR系统中存在的"语义鸿沟"问题,提出一种结合语义特征和视觉特征的图像检索方法.将图像的语义特征和视觉特征数据结合到同一个索引向量中,进行基于内容的图像检索.系统使用潜在语义索引(LSI)技术提取图像的语义特征,提取颜色直方图作为图像的视觉特征.通过将图像底层视觉特征与图像在向量空间中的语义统计特征相...     

基于综合特征的图像分割

文章提出了一种有效的基于颜色和纹理综合特征的图像分割方法。将图像以块为单位进行划分,在YUV空间,提取块的颜色特征和纹理特征,在这种综合特征基础上,采用改进的K均值聚类法进行图像分割。该方法能自适应确定聚类中的参数,且兼顾点的位置连通关系,从而达到了较好的分割效果。     

Local fractal and multifractal features for volumic texture characterization

For texture analysis, several features such as co-occurrence matrices, Gabor filters and the wavelet transform are used. Recently, fractal geometry appeared to be an effective feature to analyze texture. But it is often restricted to 2D images, while 3D information can be very important especially in medical image processing. Moreover applications are limited to the use of fractal dimension. This study focuses on the benefits of fractal geometry in a classification method based on volumic texture analysis. The proposed methods make use of fractal and multifractal features for a 3D texture analysis of a voxel neighborhood. They are validated with synthetic data before being applied on real images. Their efficiencies are proved by comparison to some other texture features in supervised classification processes (AdaBoost and support vector machine classifiers).The results showed that features based on fractal geometry (by combining fractal and multifractal features) contributed to new texture characterization. Information on new features was useful and complementary for a classification method.This study suggests that fractal geometry can provide a new useful information in 3D texture analysis, especially in medical imaging.     

Single image super resolution for texture images through neighbor embedding

This article proposes an improved learning based super resolution scheme using manifold learning for texture images. Pseudo Zernike moment (PZM) has been employed to extract features from the texture images. In order to efficiently retrieve similar patches from the training patches, feature similarity index matrix (FSIM) has been used. Subsequently, for reconstruction of the high resolution (HR) patch, a collaborative optimal weight is generated from the least square (LS) and non-negative matrix factorization (NMF) methods. The proposed method is tested on some color texture, gray texture, and some standard images. Results of the proposed method on texture images advocate its superior performance over established state-of-the-art methods.

     

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.     

基于差别特征的纹理图像识别研究

文中提出了一种基于PCNN的捕获率图的纹理图像差别特征提取的新万法。通过对PCNN的基本特性分析，指出PCNN的捕获率图不仅包含了全局图像灰度分布的统计特征，还包含了局部图像灰度的空间分布的相关统计特征，即相邻象素之间的几何信息。这恰是纹理图像的个性特征所在。实验结果证明，该方法具有良好的鲁棒性。获得这些纹理图像的特征旨在度量纹理图像的相似性或从图像中抽取纹理区域。     

结合Gabor滤波器和扩展LTP算子的无监督纹理图像分割

目的 现实中的纹理往往具有类型多样、形态多变、结构复杂等特点,直接影响到纹理图像分割的准确性。传统的无监督纹理图像分割算法具有一定的局限性,不能很好地提取稳定的纹理特征。本文提出了基于Gabor滤波器和改进的LTP（local ternary pattern）算子的针对复杂纹理图像的纹理特征提取算法。方法 利用Gabor滤波器和扩展LTP算子分别提取相同或相似纹理模式的纹理特征和纹理的差异性特征,并将这些特征融入到水平集框架中对纹理图像进行分割。结果 通过实验表明,对纹理方向及尺度变化较大的图像、复杂背景下的纹理图像以及弱纹理模式的图像,本文方法整体分割结果明显优于传统的Gabor滤波器、结构张量、拓展结构张量、局部相似度因子等纹理分割方法得到的结果。同时,将本文方法与基于LTP的方法进行对比,分割结果依然更优。在量化指标方面,将本文方法与各种无监督的纹理分割方法就分割准确度进行对比,结果表明,在典型的纹理图像上,本文方法准确度达到97%以上,高于其他方法的分割准确度。结论 提出了一种结合Gabor滤波器和扩展LTP算子的无监督多特征的纹理图像分割方法,能够较好地提取相似纹理模式的特征和纹理的差异性特征,且这些纹理特征可以很好地融合到水平集框架中,对真实世界复杂纹理图像能够得到良好的分割效果。     

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.     

基于分形理论和Kohonen神经网络的纹理图像分割方法

分形理论作为描述自然现象的一种模型,受到人们越来越多的重视。该文提出采用分形维数和多重分形广义维数谱q－D（q）作为纹理特征,采用自组织神经网络Kohonen网络作为分类器的图象分割方法。通过对纹理图象的分割实验,结果令人满意,证实该方法的有效性。