一种基于GMM的图像语义标注方法

针对图像语义概念具体语义描述的问题,提出了一种基于GMM的图像语义标注方法。该方法对于每一个语义概念分别建立基于颜色特征和纹理特征的GMM模型,利用EM算法获取关键词内容,最后融合两个GMM模型求取的概率排序结果,对未知图像进行标注。实验结果表明,提出的方法能够准确地为待标注的图像预测出若干文本关键字,有效提高图像标注的查准率和查全率。     

基于多级描述模型的渐进式图像内容理解

针对目前基于内容的图像检索技术中低级特征无法准确全面地描述高级语义的问题,本文提出了一种基于多级图像描述模型的渐进式图像内容理解.该图像描述模型在不同层次上对图像内容进行分析和提取,实现了图像内容的全方位描述,从底层向高层的过渡是渐进式的图像理解过程.特别是从视觉感知层到目标层,体现了图像低级特征与高级语义之间的过渡.本文给出了一种基于先验知识的上下文驱动的目标理解算法,实现了图像语义的提取.作为一个应用实例,本文给出了以上方法在基于内容的图像检索技术中的具体应用.     

基于子波变换的纹理图像分类

本文用子波变换的方法描述了纹理图像多尺度、多方向的特性,提出了适合于纹理图像分类的新的子波特征。通过对其稳定性和视觉特性的详细分析,指出此特征优于传统的能量特征。文章最后结合九类自然纹理图像,分别基于标准子波特征、子波包特征用BP神经网络进行了分类识别。实验结果表明,在无噪声情况下,对自然纹理图像可无误差分类;在有噪声情况下,正确分类识别率高,表现出强的稳定性。     

基于小波纹理、语义特征和相关反馈的医学图像检索

本文针对胸片和胸部CT扫描两种医学图像库，提出一种融合小波纹理、语义特征和相关反馈的医学图像检索技术。首先分析比较了纹理的三大描述方法：灰度层共现矩阵，纹理谱，小波变换，采用了频谱法中的一种基于小波变换的纹理描述方法进行纹理分析。为了进一步提高检索精度，将语义特征融合到该算法中，然后利用相关反馈技术有效地提高检索准确率。通过在原型系统上的实验，从查准率、查全率、平均序号验证了此方法的有效性。     

基于统计几何的纹理特征提取及分类算法

纹理识别是计算机视觉领域一个重要的课题,本文研究了统计几何特征(SGF)纹理分析方法并与向量机结合构建分类系统。对支持向量机(SVM)的多分类方法的实现,构建了粗分类和细分类相结合的多分类器,实现了纹理图像的准确划分,为有效纹理特征的表示奠定了基础。本文对统计几何特征提取方法进行了研究,利用图像函数图来进行纹理描述,使用一个可变的阈值把一幅灰度纹理图像切割成一系列二进制图像,由二进制图像的连通域、几何拓扑属性推导纹理描述特征。实验结果表明,统计几何特征具有非常强的纹理描述能力,同时能够克服图像的旋转。     

Image feature extraction and classification based on statistical geometrical features

纹理识别是计算机视觉领域一个重要的课题,本文研究了统计几何特征（SGF）纹理分析方法并与向量机结合构建分类系统。对支持向量机（SVM）的多分类方法的实现,构建了粗分类和细分类相结合的多分类器,实现了纹理图像的准确划分,为有效纹理特征的表示奠定了基础。本文对统计几何特征提取方法进行了研究,利用图像函数图来进行纹理描述,使用一个可变的阈值把～幅灰度纹理图像切割成一系列二进制图像,由二进制图像的连通域、几何拓扑属性推导纹理描述特征。实验结果表明,统计几何特征具有非常强的纹理描述能力,同时能够克服图像的旋转。     

基于子波变换的纹理图像分类

本文用子波变换的方法描述了纹理图像多尺度、多方向的特性,提出了适合于纹理图像分类的新的子波特征。通过对其稳定性和视觉特性的详细分析,指出此特征优于传统的能量特征。文章最后结合九类自然纹理图像,分别基于标准子波特征,子波包特用ＢＰ神经网络进行了分类识别。实验结果表明,在无噪声情况下,对自然纹理图像可无误差分类;在有噪声情况下,正确分类识别率高,表现出强的稳定性。     

面向自然场景分类的贝叶斯网络局部语义建模方法

本文提出了一种基于贝叶斯网络的局部语义建模方法.网络结构涵盖了区域邻域的方向特性和区域语义之间的邻接关系.基于这种局部语义模型,建立了场景图像的语义表述,实现自然场景分类.通过对已标注集的图像样本集的学习训练,获得贝叶斯刚络的参数.对于待分类的图像,利用该模型融合区域的特征及其邻接区域的信息,推理得到区域的语义概率;并通过网络迭代收敛得到整幅图像的区域语义标记和语义概率;最后在此基础上形成图像的全局描述,实现场景分类.该方法利用了场景内部对象之间的上下文关系,弥补了仅利用底层特征进行局部语义建模的不足.通过在六类自然场景图像数据集上的实验表明,本文所提的局部语义建模和图像描述方法是有效的.     

利用SURF和PLSA的遥感图像场景分类

针对遥感图像场景分类的特点,提出了一种基于SURF和PLSA的分类方法。该方法首先采用SURF算法提取图像的局部特征,其次对特征利用K-means聚类生成视觉词汇表,从而得到图像的视觉词袋描述。然后利用概率潜在语义分析(PLSA)从图像中提取潜在语义特征,最后使用支持向量机(SVM)分类器完成图像的场景分类任务。在21类场景图像上的实验结果表明,文中方法可以有效提高遥感图像的场景分类精确度。     

基于模糊同质性映射的文本检测方法

视频图像中的文本是从语义层次对视频图像内容进行描述的非常有效信息,文本检测为基于语义的图像检索提供了条件。该文提出了一种基于模糊逻辑和同质映射相结合的文本检测方法,首先利用最大信息熵准则将原始图像模糊化;然后构造基于边缘信息和纹理信息的图像同质性,并利用它将图像映射到模糊同质性空间;最后在模糊同质性空间通过纹理分析检测文本区域。与直接在图像空间域中提取特征的文本检测方法相比,该方法对复杂背景视频图像的文本检测取得了更好的效果,并且适用于多种类型的视频图像中文本的检测。     

Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.     

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.      

基于模糊信息原理的纹理图像分割方法

提出了一种基于同步自回归（ＳＡＲ） 模型和模糊信息原理进行纹理分割的方法。利用二阶ＳＡＲ 模型对图像纹理进行描述,用最小平方误差方法对模型参数进行估计,在对模型参数分析的基础上,将估计的参数进行改进后作为纹理的特征向量用于纹理图像的分类与分割。由于实际图像带有许多的模糊信息,组成纹理的基元和基元之间的关系也具有很大的模糊性,文中根据模糊信息原理,分析了纹理图像的模糊特性,给出了一种基于模糊贴近度的纹理分割方法。实验结果表明,与常规的距离方法相比,用文中的方法进行图像纹理分割能取得更好的效果。     

Oriented texture completion by AM-FM reaction-diffusion

We provide an automated method to repair broken, occluded oriented image textures. Our approach is based on partial differential equations (PDEs) and AM-FM image modeling. Reconstruction of the texture occurs via simultaneous PDE-generated diffusion and reaction. In the diffusion process, the image is adaptively smoothed, preserving important boundaries and features. The reaction process produces the reconstructed textural information in the occluded image regions. Gabor (1946) filters are designed and used in the reaction process using an AM-FM dominant component analysis. An AM-FM model of the texture image is constructed, making it possible to localize the reaction filters spatio-spectrally. In contrast to previous disocclusion techniques that depend on interpolation, on continuity of the connected components within the image level sets, or on texture estimation, the reaction-diffusion process proposed here yields a seamless transition between the recreated region and the unoccluded image regions. Using AM-FM dominant component analysis, we avoid the ad hoc parameter selection typified with other reaction-diffusion approaches. As a useful example, we focus on the repair of broken, occluded fingerprints. We also treat several exemplary natural textures to demonstrate the technique's generality     

自然纹理分类和识别方法初探

纹理图像千变万化，目前在纹理分类上没有明确的标准。文章借用自然语言中的概念词对自然纹理进行基于概念的分类，把自然纹理分成花纹、条纹、鱼鳞、波纹、斑纹、木纹、裂纹、绒毛、颗粒等九大类别，建立了自然纹：理图像库，并用小波变换提取纹理特征，对这些特征进行基于支持向量机的分类。实验结果表明该分类和识别方法准确率高，可以考虑作为基于图像内容检索的一种方法。     

Optimal Gabor filters for texture segmentation

Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs.     

Adaptive perceptual color-texture image segmentation.

We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.     

SVD-based modeling for image texture classification using wavelet transformation.

This paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition. The probability density function of the singular values of wavelet transformation coefficients of image textures is modeled as an exponential function. The model parameter of the exponential function is estimated using maximum likelihood estimation technique. Truncation of lower singular values is employed to classify textures in the presence of noise. Kullback-Leibler distance (KLD) between estimated model parameters of image textures is used as a similarity metric to perform the classification using minimum distance classifier. The exponential function permits us to have closed-form expressions for the estimate of the model parameter and computation of the KLD. These closed-form expressions reduce the computational complexity of the proposed approach. Experimental results are presented to demonstrate the effectiveness of this approach on the entire 111 textures from Brodatz database. The experimental results demonstrate that the proposed approach improves recognition rates using a lower number of parameters on large databases. The proposed approach achieves higher recognition rates compared to the traditional sub-band energy-based approach, the hybrid IMM/SVM approach, and the GGD-based approach.     

基于小波域和活动轮廓模型的纹理图像分割

提出一种双树复小波域局部二值模式和活动轮廓模型的纹理图像分割方法.该方法首先使用双树复合小波分解纹理图像,然后使用局部二值模式提取纹理特征.利用最大熵准则对纹理特征图像进行选择,活动轮廓模型用于最后的分割.实验结果表明提出的方法对于合成纹理和自然场景数据集,达到了较高的分割精度.     

Effective image retrieval based on hidden concept discovery in image database.

This paper addresses content-based image retrieval in general, and in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation, a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which the expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10000 general-purposed images demonstrate its promise and effectiveness.