一种基于一致性邻域超图模型的图象分割方法

提出了一种将超图理论与图象的空 -频域特征分析相结合的图象分割方法 .该方法是基于图象的多分辨率小波分析及高斯 -马尔可夫随机场理论 ,在抽取一组反映图象局部空间结构信息的特征矢量基础上 ,根据同一区域的象素具有相似的特征矢量的原则 ,将图象转换为一个关于特征相似性测度的邻域超图 ,再利用覆盖 -选择算法对该超图进行分割的方法 .实验证明 ,该方法具有较好的稳定性和适应性 ,尤其对于一些信噪比较低的图象 ,也具有良好的分割效果 .     

一种肺部肿瘤CT图象序列的自动分割方法

肺部肿瘤序列图象的自动分割是计算机肺部肿瘤三维辅助诊断系统的关键技术之一,肿瘤与周围组织关系的复杂性造成分割困难.为了给医生提供准确的肺部肿瘤影像,运用纹理分析和径向基神经网络实现了肺部肿瘤CT图象序列的自动分割,并根据相邻层肿瘤图象灰度、位置的相关性,提出了一种自动获取多层肿瘤区域神经网络训练样本的阈值分割算法.该算法首先计算图象纹理统计参数,以组成特征矢量空间,然后利用自适应径向基神经网络对特征矢量进行分类来实现肿瘤序列图象的自动分割.实验结果表明,与基于灰度的区域增长法和基于梯度算子和形状算子的最优阈值的分割方法相比较,该方法不仅能充分利用肺部肿瘤序列图象的三维信息,还可最大限度地减少人工干预,且分割结果较好地表现了肿瘤形态特征,经临床医生评估,具有较好的临床指导价值.     

空间灰度相关图象纹理分割方法

本文给出了图象纹理分割的空间灰度相关法(SGLDM)中四个描述性强的纹理特征。定义了纹理特征矢量。在此定义基础上,给出了一种新的图象纹理分割方法。最后以四幅分割难度较大的纹理图象实验,说明利用四种纹理特征的方法可以有效地对纹理子图案非随机旋转的图象进行纹理分割。     

基于小波模糊聚类的均质纹理和非均质纹理图象检索

基于内容的图象检索是近年来的研究热点 ,为此提出了一种自动区分均质纹理和非均质纹理图象 ,并对这两类图象分别进行检索的算法 .算法首先从图象离散小波变换的低频子带提取一定的颜色和纹理特征用于模糊聚类 ,将图象的低频子带分割为一定的区域 ;然后根据分割的结果将图象自动语义分类为均质纹理或者非均质纹理图象 ;最后对均质纹理和非均质纹理图象分别提取不同的特征矢量 ,并按照一定的相似度准则检索图象 .实验结果表明 ,该算法具有良好的均质纹理和非均质纹理图象分类和检索性能 .     

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。     

一种新的结合纹理特征的SVM图象分割方法

本文提出了一种新的结合纹理特征的支持向量机图象分割方法,将纹理特征和灰度特征一起组成训练特征向量,利用支持向量机分类方法进行图象分割.该算法结合了纹理特征在图象描述中的重要意义和支持向量机方法在模式识别领域已表现出的优越性能,实验证明其在图象分割中取得了良好的效果.同时,当需要处理一批内容相似,感兴趣区域具有相同纹理、灰度特征的同类图象时,只需对其中一幅代表性的图象进行SVM训练,所产生的分类模型适用于所有该类图象,无需逐幅进行处理,大大简化了运算过程.     

纹理图象分割的分形方法研究

在分形维数的基础上研究了将其用于纹理分割的方法。采用差分盒维数（ＤＢＣ）方法和一种改进的边缘保持算法计算象素点的分形维数ＦＤ，基于原始图象的方向性差分和多重分形的概念提取出一组特征，并将Ｋｏｈｏｎｅｎ的ＳＯＦＭ网用于对得到的图象特征矢量进行分类，得到了较好的纹理图象分割效果。最后和特征平滑与Ｋ均值聚类方法的结果进行了比较     

基于小波分形特征提取的图象分割方法

提出了一种基于小波分解和分形纹理特征计算的图象分割方法,首先考虑对图象进行小波变换,然后对不同通道的子图象提取纹理的分形特征和能量特征,最后用直方图阈值分割方法实现图象的分割,实验表明,该方法对模拟纹理图象以及多少谱遥感图象的分割都取得了满意的效果。     

基于纹理方向整体分布特征的台风自动识别方法

为实现卫星云图上台风的自动识别，提出了一种基于纹理方向整体分布特征的台风云系图象自动识别方法，通过引入矢量矩的概念来表现图象纹理整体分布规律，该识别方法采用全局搜索方式，将一窗口在整幅图象上滑动，首先计算出窗口图象内各点的纹理方向，进而得出窗口图像的矢量矩，将矢量矩与阈值比较来判整幅图象是否为台风云系图像，实验结果表明，该方法能够识别不同类型和不同发展阶段的台风云系图象，能够很好地将台风云系与其他干扰云系区分开，具有较广泛的适应性和较高的识别率。     

一种基于空间特征矢量的图像分割方法

针对灰度图像,提出了一种基于空间特征矢量的图像分割方法,构建了以像素的灰度、梯度、像素r的邻域均值为特征的三维特征空间,并将图像像素点对应空间特征点。通过计算像素特征矢量与阈值特征矢量的矢量差,求出矢量差与阈值特征矢量的夹角,然后比较夹角与动态分割参数的关系,判定像素所在区域(目标或背景)。实验表明,该方法可以较快速地实现图像分割,分割的效果也比较好。     

A reductive approach to hypergraph clustering: An application to image segmentation

In the last few years, hypergraph-based methods have gained considerable attention in the resolution of real-world clustering problems, since such a mode of representation can handle higher-order relationships between elements compared to the standard graph theory. The most popular and promising approach to hypergraph clustering arises from concepts in spectral hypergraph theory [53], and clustering is configured as a hypergraph cut problem where an appropriate objective function has to be optimized. The spectral relaxation of this optimization problem allows to get a clustering that is close to the optimum, but this approach generally suffers from its high computational demands, especially in real-world problems where the size of the data involved in their resolution becomes too large. A natural way to overcome this limitation is to operate a reduction of the hypergraph, where spectral clustering should be applied over a hypergraph of smaller size. In this paper, we introduce two novel hypergraph reduction algorithms that are able to maintain the hypergraph structure as accurate as possible. These algorithms allowed us to design a new approach devoted to hypergraph clustering, based on the multilevel paradigm that operates in three steps: (i) hypergraph reduction; (ii) initial spectral clustering of the reduced hypergraph and (iii) clustering refinement. The accuracy of our hypergraph clustering framework has been demonstrated by extensive experiments with comparison to other hypergraph clustering algorithms, and have been successfully applied to image segmentation, for which an appropriate hypergraph-based model have been designed. The low running times displayed by our algorithm also demonstrates that the latter, unlike the standard spectral clustering approach, can handle datasets of considerable size.     

Hypergraph imaging: an overview

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation.     

Adaptive hypergraph superpixels

Superpixel segmentation, which amounts to partitioning an image into a number of superpixels each of which is a set of pixels sharing common visual meanings, requires specific needs for different computer vision tasks. Graph based methods, as a kind of popular superpixel segmentation method, regard an image as a weighted graph whose nodes correspond to pixels of the image, and partition all pixels into superpixels according to the similarity between pixels over various feature spaces. Despite their improvement of the performance of segmentation, these methods ignore high-order relationship between them incurred from either locally neighboring pixels or structured layout of the image. Moreover, they measure the similarity of pairwise pixels using Gaussian kernel where a robust radius parameter is difficult to find for pixels which exhibit multiple features (e.g., texture, color, brightness). In this paper, we propose an adaptive hypergraph superpixel segmentation (AHS) of intensity images for solving both issues. AHS constructs a hypergraph by building the hyperedges with an adaptive neighborhood scheme, which explores an intrinsic relationship of pixels. Afterwards, AHS encodes the relationship between pairwise pixels using characteristics of current two pixels as well as their neighboring pixels defined by hyperedges. Essentially, AHS models the relationship of pairwise pixels in a high-order group fashion while graph based methods evaluate it in a one-vs-one fashion. Experiments on four datasets demonstrate that AHS achieves higher or comparable performance compared with state-of-the-art methods.     

Salient object detection using content-sensitive hypergraph representation and partitioning

As an important problem in image understanding, salient object detection is essential for image classification, object recognition, as well as image retrieval. In this paper, we propose a new approach to detect salient objects from an image by using content-sensitive hypergraph representation and partitioning. Firstly, a polygonal potential Region-Of-Interest (p-ROI) is extracted through analyzing the edge distribution in an image. Secondly, the image is represented by a content-sensitive hypergraph. Instead of using fixed features and parameters for all the images, we propose a new content-sensitive method for feature selection and hypergraph construction. In this method, the most discriminant color channel which maximizes the difference between p-ROI and the background is selected for each image. Also the number of neighbors in hyperedges is adjusted automatically according to the image content. Finally, an incremental hypergraph partitioning is utilized to generate the candidate regions for the final salient object detection, in which all the candidate regions are evaluated by p-ROI and the best match one will be the selected as final salient object. Our approach has been extensively evaluated on a large benchmark image database. Experimental results show that our approach can not only achieve considerable improvement in terms of commonly adopted performance measures in salient object detection, but also provide more precise object boundaries which is desirable for further image processing and understanding.     

Interactive image segmentation using probabilistic hypergraphs

This paper introduces a novel interactive framework for segmenting images using probabilistic hypergraphs which model the spatial and appearance relations among image pixels. The probabilistic hypergraph provides us a means to pose image segmentation as a machine learning problem. In particular, we assume that a small set of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels by learning on a hypergraph via minimizing a quadratic smoothness term formed by a hypergraph Laplacian matrix subject to the known label constraints. We derive a natural probabilistic interpretation of this smoothness term, and provide a detailed discussion on the relation of our method to other hypergraph and graph based learning methods. We also present a front-to-end image segmentation system based on the proposed method, which is shown to achieve promising quantitative and qualitative results on the commonly used GrabCut dataset.     

基于局部能量特征的视频字幕分割

视频字幕能够给人们提供高度浓缩的与视频内容相关的信息 ,但字幕通常均以图象方式存在于视频、图象中 ,为了提取视频字幕 ,提出了一种基于局部能量的视频字幕分割方法 ,该方法利用局部能量与图象中边缘、轮廓特征之间的对应关系进行字幕的自动分割 .为了快速地进行局部能量的计算 ,在分析局部能量模型基础上 ,将局部能量计算方法进行了推广 ,即先通过选用具有对称性的双正交小波基及 Hilbert变换 ,构造了适于局部能量计算的 90°相移滤波器 ,然后通过多分辨率小波变换实现了信号的多分辨率频带分割 ,并在此基础上计算局部能量 .实验结果表明 ,该方法可获得较好的分割效果     

基于Gabor小波滤波器的遥感图像多频道纹理分析

提出了一种基于Gabor小波滤波器的遥感图像多频道纹理分析算法，大量实验证明本算法方便有效地解决了由于遥感图像目标与背景对比度差、图像边缘模糊、噪声较大而给图像处理带来的困难。     

Probabilistic hypergraph based hash codes for social image search

With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing（SH） has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.