一个智能化的图像检索系统IIRS

本文深入研究了支持语义查询的大型图像数据库检索技术，提出了一种新的基于内容的图像检索系统：IIRS(Intelligent Irnage Retrieval System)。该系统支持多种特征的组合查询，引入了反馈机制及语义查询，具有自动学习及再学习的能力。实验结果表明，引入相关反馈及语义查询，可大大提高检索性能，     

图像数据库分类的自组织技术

本文采用 Kohoenmap自组织神经网络技术 ,提出一种依据检索特征对图像数据库进行预分类的方法 .在文中利用这种预分类实现按类别浏览图像数据库 (image browsing by category)的功能 ,提出一种层次化的类 SS- tree的二分索引结构 .实现建立在预分类基础上的示例查询 .这种查询方法与穷搜索方法相比 ,具有更高的检索效率 ,检索次数随着库规模的增加不会显著增多 .基于图像库的实验验证了分类算法和检索算法的有效性     

基于对象的彩信图像检索系统的研究与实现

基于对象的彩信图像检索系统是继图像检索和彩信检索结合的新一代信息检索技术,介绍了目前热门的基于对象的图像检索系统的概念,以及应用到彩信检索领域的工作原理.在基于对象的图像检索基础上,加入了对图像中区域对象的识别过程,并将识别结果存入图像属性索引数据库,然后在检索过程中根据用户输入的对象查询请求,到数据库里进行检索,最终将检索结果返回给彩信用户.     

改进的基于支持向量机的Web图像检索相关反馈方法

结合用户相关反馈的超平面查询在基于内容的图像检索中面临数据不平衡等问题,即对于给定的查询,图像数据库中负样例(与查询无关的图像)数目往往远大于正样例,使得传统的用户相关反馈技术难以获得足够的相关图像,影响了超平面查询的性能.提出一种新的基于支持向量机的Web图像检索的主动学习策略,根据Web图像的URL、视觉特征来估计图像与查询样本的相关性,用潜在的正样例图像来弥补图像数据的不平衡性,并且提出了对SVM返回结果的重新排序方法.对10000多幅来自50多个不同网站的Web图像数据进行了实验.实验结果显示,与传统的方法相比,该方法对检索性能有明显的提高.     

一种基于内容的图像检索界面

基于内容和对象的图像压缩和检索是下一代的图像处理技术,具有较广阔的应用前景。目前该领域的研究主要从设计方便、快捷的用户查询界面和发展图像数据库检索技术两方面展开。为此,该文提出了一种基于内容的图像检索用户界面的设计方法来满足用户复杂的检索要求。在图像的检索过程中,通过用户组合图标的方法来描述检索要求,同时将图像的颜色和空间信息相结合进行图像查询,并借助用户的反馈信息实现系统的自学习功能,最终逐步提高系统图像检索的速度和准确性。     

PB数据库图像存储技术研究

为了保证图像或图纸文件数据的安全,采用数据库的BLOB字段来存储这些数据,并支持查询时显示和文件复原。文章利用PB编程语言Oracle9i数据库存储技术,结合人力资源管理软件中的需求,对图像选取、图像存储、图像显示等技术进行了研究,实现了数据库图像字段的存储和检索。     

PB数据库图像存储技术研究

为了保证图像或图纸文件数据的安全,采用数据库的BLOB字段来存储这些数据,并支持查询时显示和文件复原。文章利用PB编程语言Oracle9i数据库存储技术．结合人力资源管理软件中的需求,对图像选取、图像存储、图像显示等技术进行了研究,实现了数据库图像字段的存储和检索。     

基于HSV颜色直方图的服装检索

服饰是人类特有的劳动成果,它既是物质文明的结晶,又具精神文明的含意。基于内容的图像检索是当前国内外的一个研究热点。这种查询过程充分体现了图像的信息特点又结合了图像处理、图像理解、数据库技术、模式识别技术和良好的人机交互技术,是一项在理论研究和实际应用中都极有前途的新技术。在本课题中,开发一个以服装颜色为特征的服装图像检索工具,提供服装检索功能。     

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。     

基于LSI的图像语义检索

图像语义检索是实现图像快速、直观、准确查询的有效办法,针对目前直接采用图像低层特征无法满足图像语义查询的问题,该文提出了一种利用LSI技术,实现图像语义的检索方法。     

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.     

小波域多方向信息融合的纹理图像检索

由于能提供较多的方向信息,双树复小波变换在纹理图像检索中的检索率高于传统小波变换,但传统小波变换与双树复小波变换得到的方向子带不同。针对该问题,提出一种融合传统小波和双树复小波变换的一阶统计信息从而提取特征进行纹理图像检索的方法。对Brodatz图像库的仿真实验表明,该方法优于传统小波和双树复小波方法。     

Expert system design using wavelet and color vocabulary trees for image retrieval

A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper. The characteristics (vector points) of image are computed using color (color histogram) and SOT (spatial orientation tree). The SOT defines the spatial parent-child relationship among wavelet coefficients in multi-resolution wavelet sub-bands. First the image is divided into sub-blocks and then constructed the SOT for each low pass wavelet coefficient is considered as a vector point of that particular image. Similarly the color histogram features are collected from the each sub-block. The vector points of each image are indexed using vocabulary tree. The retrieval results of the proposed method are tested on different image databases, i.e., natural image database consists of Corel 1000 (DB1), Brodatz texture image database (DB2) and MIT VisTex database (DB3). The results after being investigated show a significant improvement in terms of average precision, average recall and average retrieval rate on DB1 database and average retrieval rate on texture databases (DB2 and DB3) as compared with most of existing techniques on respective databases.     

Wavelet correlogram: A new approach for image indexing and retrieval

In this paper, a new algorithm for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution image decomposition and color correlation histogram. According to the new algorithm, wavelet coefficients of the image are computed first using a directional wavelet transform such as Gabor wavelets. A quantization step is then applied before computing one-directional autocorrelograms of the wavelet coefficients. Finally, index vectors are constructed using these one-directional wavelet correlograms. The retrieval results obtained by application of our new method on a 1000 image database demonstrated a significant improvement in effectiveness and efficiency compared to the indexing and retrieval methods based on image color correlogram or wavelet transform.     

基于内容的MRI脑肿瘤图像特征提取及检索方法

医学图像检索中特征提取方法对检索的效果、性能具有重要影响,针对这个问题,设计了一个基于内容的医学图像检索系统.为了给医学图像检索系统的临床应用提供参考价值,该系统以哈佛大学医学院开发的脑肿瘤MRI医学图像数据库为背景,比较了颜色相关图、颜色矩、灰度共生矩阵、金字塔小波变换和树型小波变换这5个特征提取技术对MRI脑肿瘤医学图像的检索性能.实验结果表明树型小波变换和金字塔小波变换的检索效果较好.     

Content-based image indexing and searching using Daubechies' wavelets

This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval, a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two-level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. WBIIS is much faster and more accurate than traditional algorithms. When tested on a database of more than 10 000 general-purpose images, the best 100 matches were found in 3.3 seconds.     

Scalable color image indexing and retrieval using vector wavelets

This paper presents a scalable content-based image indexing and retrieval system based on vector wavelet coefficients of color images. Highly decorrelated wavelet coefficient planes are used to acquire a search efficient feature space. The feature space is subsequently indexed using properties of all the images in the database. Therefore, the feature key of an image not only corresponds to the content of the image itself but also to how much the image is different from the other images being stored in the database. The search time linearly depends on the number of images similar to the query image and is independent of the database size. We show that, in a database of 5,000 images, query search takes less than 30 msec on a 266 MHz Pentium II processor, compared to several seconds of retrieval time in the earlier systems proposed in the literature     

Adaptive lifting for shape-based image retrieval

We propose to use adaptive wavelet lifting for image retrieval systems that are based on shape detection and multiresolution structures of objects in a database against a background of texture. To measure the performance of our approach, feature vectors are computed based on moment invariants of detail coefficients produced by the adaptive lifting scheme and retrieval rates are obtained by measuring distances between these vectors. Retrieval rates are compared with the rates obtained when using non-adaptive wavelet filtering as a preprocessing step. A synthetic database is created for this simulation.     

基于纹理-形状特征的图像检索技术

图像数据库应用日益广泛,如何高效、准确地进行图像的检索成为一项重要的研究领域。传统的图像检索主要依赖颜色、纹理、形状、空间关系等单一视觉特征,检索效果往往不够理想。针对这一实际问题,提出一种新的图像检索方法,通过对图像进行小波变换,获得纹理和形状的综合特征,对综合特征进行归一化处理,并将其作为图像相似性的衡量依据。实验证明基于纹理和形状特征的图像检索具有很好的效果。