A fractal-based clustering approach in large visual database systems

Large visual database systems require effective and efficient ways of indexing and accessing visual data on the basis of content. In this process, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexed to assist efficient access to image content. With the large volume of visual data stored in a visual database, image classification is a critical step to achieve efficient indexing and retrieval. In this paper, we investigate an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to determine the degree of their similarity. Images in a visual database can be categorized in clusters on the basis of their similarity to a set of iconic images. Classification metrics are proposed for the measurement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicability of these metrics and the proposed clustering technique to various visual database applications.     

A Multi-Resolution Content-Based Retrieval Approach for Geographic Images

Current retrieval methods in geographic image databases use only pixel-by-pixel spectral information. Texture is an important property of geographical images that can improve retrieval effectiveness and efficiency. In this paper, we present a content-based retrieval approach that utilizes the texture features of geographical images. Various texture features are extracted using wavelet transforms. Based on the texture features, we design a hierarchical approach to cluster geographical images for effective and efficient retrieval, measuring distances between feature vectors in the feature space. Using wavelet-based multi-resolution decomposition, two different sets of texture features are formulated for clustering. For each feature set, different distance measurement techniques are designed and experimented for clustering images in a database. The experimental results demonstrate that the retrieval efficiency and effectiveness improve when our clustering approach is used.     

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.     

基于颜色-空间特征的图像检索

虽然基于颜色直方图特征的图像检索方法简单、高效,但却丢失了颜色的空间分布信息.提出了一种基于颜色-空间特征的图像检索方法.该方法将图像内容看成由若干对象组成的集合,首先利用图像分割得到主要对象,然后根据对象的颜色、位置和形状特征计算图像间内容的相似度,再进行检索.实验结果表明,当图像中有明显的物体时,该方法与颜色直方图相比,能够更加准确和高效地查找出用户所需内容的图像,明显地提高了检索精度.     

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。     

改进五叉树分解法在遥感图像检索中的应用

由于一幅遥感图像是对一定范围内的地表状态的成像,并且遥感图像具有多样性、复杂性、海量等性质,致使遥感图像检索往往是查询图像和图像库图像的局部区域之间的相似性匹配。为了提高遥感图像的检索效率,必须首先对遥感图像进行分解。提出了一种将遥感图像分层分解的遥感图像检索方法,该方法利用改进五叉树分解法将图像库图像按层次分解成不同大小的子图,在提取子图的纹理特征后,以查询图像和图像库子图之间的欧式距离衡量图像相似度,实现了遥感图像检索。利用海地地震时的航空遥感图像作为实验数据,应用改进五叉树分解法将遥感图像分解后,进行查询检索实验,并与普通五叉树进行了对比。实验结果表明利用改进五叉树分解法进行遥感图像分解后得到的分块图像,可以更精准地查询出用户真正感兴趣的部分,能够获得较高的查全率和查准率,提高查询效率。     

A multiscale approach to texture-based image retrieval

This paper presents research on a robust technique for texture-based image retrieval in multimedia museum collections. The aim is to be able to use a query image patch containing a single texture to retrieve images containing an area with similar texture to that in the query. The feature extractor used to build the feature vectors is based on an improved version of the discrete wavelet frames (DWF), proposed elsewhere. In order to utilise the feature extractor on real scene image datasets, a block-oriented decomposition technique, termed the multiscale sub-image matching method, is presented. The multiscale method, together with the DWF, provide an efficient content-based retrieval technique without the need for segmentation. The algorithms are tested on a range of databases of texture images as well as on real museum image collections. Promising results are reported.

基于色彩的图像数据库检索方法的研究

文中论述了基于色彩的图像检索方法,对色彩空间的选择、彩色聚类方法、色彩直方图的距离、以及基于图像分割的色彩直方图等方面进行了详细的论述;为提高图像检索的效率和精度,采用了多阶段相似比较的方法。     

基于内容的图象检索技术

随着数字图象的日益增多,基于内容的图象检索已成为图象使用者和管理者迫切需要解决的问题,近年来,各国研究者纷纷加入该领域的研究.为了使人们对该领域现状有个概略了解,以推动该领域研究进一步开展,首先概括介绍了基于内容图象检索的产生、发展及其关键技术;然后介绍了特征提取(包括低层特征和语义特征)及其相似性计算、相关反馈等的原理及算法;最后指出了基于内容的图象检索技术与计算机视觉技术的区别所在,并对目前存在的问题和应着重的研究内容以及发展方向进行了分析.     

图像检索中融合语义和底层特征方法的研究

针对基于内容的图像检索系统检索效率不高的情况,通过在低层视觉特征上提取图像的颜色和纹理特征和空间信息,综合图像的语义特征,实现了对图像数据库的检索,最后,为了提高检索效率,把相关反馈技术引入到图像检索系统中。实验证明,该方法取得了较好的检索查全率和准确率。     

Color indexing for efficient image retrieval

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size.In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that usesR-tree. The worst case search time complexity of this approach isOn _q log(N^* n_avg)), whereN is the number of images in the database, andn _q andn _avg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.     

基于新的空间关系特征的图像检索方法

图像与图像之间没有清晰的空间结构,这样就不能有效利用图像间空间结构上的相关性信息,针对此问题提出一种基于新的空间关系特征的图像检索方法。首先,提取待查询图像在内的全部图像的特征向量。然后,计算特征向量每两个之间的相似性,形成相似性矩阵。将相似性矩阵的列集合作为新特征向量,命名为新的空间关系特征向量,从而将原来的特征向量映射到一个欧氏空间上。最后,在新特征空间上计算相似性,特征向量之间的相似性问题就转化为新的空间关系特征向量之间的相似性问题。在新特征空间上,图像与图像之间的空间结构变得清晰了,有利于图像检索准确度的提高。在Corel数据库上进行实验,所提方法在平均检索查准率、查全率-查准率和可视化评价指标上都优于基于颜色直方图的图像检索方法。结果表明,基于新的空间关系特征的图像检索方法有效利用了图像间空间结构上的相关性信息,具有更好的检索效果。     

一种有效的支持海量图像数据库QBE查询的聚类索引算法

对海量图像数据进行基于内容的查询与检索有赖于高效的索引和检索机制。因此,如何将海量图像数据进行合理的分类,人而建立相应的索引机制就成为了一个亟待解决的问题。本文提出了一种有效的支持海量图像数据库QBE查询的聚类索引算法。实验在1万多幅的图像数据库上进行了反复测试,结果表明该算法可以极大地提高检索效率。     

利用独立分量分析的运动模糊图像检索

研究了独立分量分析（ICA）算法在运动模糊图像检索中的应用。首先,对图片库中的图像进行ICA处理,构造由相互独立的基向量构成的子空间,将图片库中的图像及运动模糊图像分别向该空间投影,获得各自的特征。其次,利用特征向量间的余弦距离作为相似度度量标准,根据最近邻准则进行特征匹配与图像检索。最后,对人为加入高斯噪声、进行45°和90°旋转的运动模糊以及缺损图像进行了匹配检索实验。实验结果表明,利用ICA算法提取出的特征可以准确地检索出运动模糊图像的原图像,并且对噪声污染、旋转变换和图像缺损具有良好的鲁棒性。     

基于二值信息的颜色和形状特征的图像检索

由于单一特征不足以准确地描述图像,提出了一种结合颜色、形状特征的图像检索方法.提出了新的用二值信息来表示图像的主色、全局色和形状特征的方法,并由此特征构造两个过滤器快速地过滤图像库中明显不相同的图像,以提高检索速度;采用改进的颜色直方图和形状基本特征进行相似度计算,为进一步提高图像检索的质量引入相关反馈机制,提出了一种动态调整两幅图像相似度中颜色特征和形状特征的权值系数的方法.文中方法与其它方法进行了比较实验,结果表明,该方法优于其它方法.     

综合颜色和纹理特征的图像检索算法

基于内容的图像检索是当前多媒体信息检索的热点之一。基于内容的图像检索技术是根据对图像内容(特征)的描述和提取,在图像库中找到具有指定内容(特征)的图像。本文对图像颜色特征和纹理特征的提取、相似性度量等基于内容的图像检索的关键技术进行了分析和研究,并在此基础上,提出了一个基于颜色特征和纹理特征的图像检索算法并验证了其有效性。该算法采用HSV颜色空间的直方图作为颜色特征向量,采用灰度共生矩阵的四个纹理特征:能量、熵、惯性矩和相关性构成纹理特征向量,采用欧氏距离进行相似性度量。实验结果表明,该算法实现的系统具有良好的图像检索功能。     

Integrated probability function and its application to content-based image retrieval by relevance feedback

In the last few years, we have seen an upsurge of interest in content-based image retrieval (CBIR)—the selection of images from a collection via features extracted from images themselves. Often, a single image attribute may not have enough discriminative information for successful retrieval. On the other hand when multiple features are used, it is hard to determine the suitable weighing factors for various features for optimal retrieval. In this paper, we present a relevance feedback framework with Integrated Probability Function (IPF) which combines multiple features for optimal retrieval. The IPF is based on a new posterior probability estimator and a novel weight updating approach. We perform experiments on 1400 monochromatic trademark images have been performed. The proposed IPF is shown to be more effective and efficient to retrieve deformed trademark images than the commonly used integrated dissimilarity function. The new posterior probability estimator is shown to be generally better than the existing one. The proposed novel weight updating approach by relevance feedback is shown to be better than both the existing scoring approach and the existing ratio approach. In experiments, 95% of the targets are ranked at the top five positions. By two iterations of relevance feedback, retrieval performance can be improved from 75% to over 95%. The IPF and its relevance feedback framework proposed in this paper can be effectively and efficiently used in content-based image retrieval.     

基于张量积扩散与纹理元相似的医学图像检索

针对医学图像检索中相似性表达的自身困难,以及噪声影响的问题,提出一种通过张量积图进行扩散,利用其他数据点的上下信息改进基于纹理元的成对相似性度量的方法。首先,采用纹理元的统计方法进行医学图像特征描述和提取,并通过对纹理元相似性加权,得到图像的成对相似性;然后,利用张量积图沿着数据点的内在流形进行相似性的传播,实现全局的相似性度量。在ImageCLEFmed 2009上的实验结果表明,该算法与基于Gabor的检索算法相比,其类平均精度提高了32%,与基于尺度不变特征转换(SIFT)的检索算法相比,其类平均精度提高了19%,能良好地应用于医学图像检索。     

彩色图象的联合分布表示及检索技术

随着图象数据的大量涌现,基于内容的图象检索技术已成为图象数据库领域的研究热点,在图象检索系统中,由于颜色直方图方法简单方便,所以它已成为CBIR系统中最常用的一种技术方法,然而,经典的颜色直方图方法存在诸多缺陷,例如它不能表示图象中的空间分布信息。为此,人们提出了直方图细化技术,即将图象的颜色分布表示扩充成为颜色和其他相关特征的联合分布。为了进一步提高图象检索能力,在分析图象特征的基础上,给出了两种加权直方图模型;其一是将图象的颜色分布和细节信号能量的分布集成到单个直方图之中;另一种模型是将图象颜色及其边界强度的联合分布集成到一个直方图中。这两种方法不仅保持了经典直方图简单方便的特点;同时又有效地将空间信息集成到直方图中,实验结果表明,这些加权直方图表示均具有较强的图象辨别能力。