A New Indexing Scheme for Content-Based Image Retrieval

We propose a new efficient indexing scheme, called the HG-tree, to support content-based retrieval in image databases. Image content is represented by a point in a multidimensional feature space. The types of queries considered are the range query and the nearest-neighbor query, both in a multidimensional space. Our goals are twofold: increasing the storage utilization and decreasing the area covered by the directory regions of the index tree. The high storage utilization and the small directory area reduce the number of nodes that have to be touched during the query processing. The first goal is achieved by suppressing node splitting if possible, and when splitting is necessary, converting two nodes into three. This is done by proposing a good ordering on the directory nodes. The second goal is achieved by maintaining the area occupied by the directory region as small as possible. This is done by introducing the smallest interval that encloses all regions of the lower nodes. We note that there is a trade-off between the two design goals, but the HG-tree is so flexible that it can control the trade-off to some extent. We present the design of our indexing scheme and associated algorithms. In addition, we report the results of a series of tests, comparing the proposed index tree with the buddy-tree, which is one of the most successful point indexing schemes for a multidimensional space. The results show the superiority of our method.     

基于内容的图像检索系统的最新进展

该文在分析现有的基于内容的图像检索系统结构的基础上,探讨了系统中的关键技术和当前主要优化方法,介绍了目前的系统应用情况,着重归纳了图像检索系统研究的最新进展,并详细阐明了当前研究中存在的问题,最后指出了今后进一步研究的方向。     

基于内容的图像检索技术研究

随着数字图像在多媒体领域的广泛应用,对基于内容的图像检索技术的需求也不断增加.基于内容的图像检索技术总体上可以分为两部分:图像特征提取、图像特征的索引与匹配.图像特征提取主要解决如何在数学上有效地描述一幅图像.文中分别介绍了颜色、形状和纹理特征提取算法近年来的研究成果.图像特征索引与匹配,主要解决如何根据特征描述判断图像间的相似程度,并准确、快速列出图像库中与检索图像相似的图像,分别介绍了相似度测量方法、聚类与分类技术、相关反馈技术三类技术的主要研究成果.最后对基于内容的图像检索技术的研究难点进行了讨论,对未来可能的研究方向进行了展望.     

Key Points-based Indexing for Pre-attentive Similarities: The KIWI System

In this paper, we introduce the general architecture of an image-search engine based on pre-attentive similarities. Local features are computed in key points to represent local properties of the images. The location of key points, where local features are computed, is discussed. We present two new key point detectors designed for image retrieval, both based on multi-resolution: the contrast-based point detector, and the wavelet-based point detector. Four different local features are used in our system: differential invariants, texture, shape and colour. The local information computed in each key point is stored in 2D histograms to allow fast querying. We study the choice of the key points detector depending on the feature used, for different test sets. The Harris corner detector is used for benchmarking. Uniformly distributed points are also used, and we conclude for which applications they are effective. Finally, we show that point detector and feature efficiency depend upon the test set studied.     

利用相关反馈技术的图像检索研究

介绍了一种支持语义的图像检索系统——PICsearch(PICTURE Search),该系统获取图像低层特征(颜色)时采用基于区域的主颜色提取算法,综合考虑了图像的像素统计特征和空间位置信息同时节省存储空间和计算时间.提出了高级视觉特征的语义查询.在图像库上构建一个可扩展的语义网络,利用一种基于用户相关反馈的机器学习策略来改进这种语义网络,以解决低层特征向高层语义特征的过渡问题,使检索能够体现高层次语义属性.实验证明,PICsearch能有效通过人机协同工作,弥补了计算机理解能力的不足,提高了检索效率.     

一种新的基于SVM的相关反馈图像检索算法

提出了一种新的基于支持向量机(SVM)的相关反馈图像检索算法。实验结果表明,该算法在一定程度上解决了基于SVM的相关反馈图像检索中存在的样本不足的困难,提高了系统的检索性能。     

基于内容的图像检索技术

随着多媒体技术的迅速发展,传统的基于关键字的信息检索技术已逐渐不能满足要求,因此,基于内容的图像检索技术成为当今的一个研究热点。介绍了基于内容图像检索系统的基本组成。综述了基于颜色、纹理、形状、语义等图像检索技术的主要方法。分析和比较了现有的各种图像检索技术的方法。讨论了相关反馈技术、检索性能的评价等CBIR研究中的关键问题。同时指出了CBIR研究中存在的问题,以及未来的发展趋势和研究方向。     

一种基于SVM的相关反馈图像检索算法

相关反馈技术是近年来图像检索中的研究热点,本文以MPEG-7的边缘直方图作为图像特征,以支持向量机（SVM）为分类器,提出一种新的相关反馈算法。在每次反馈中对用户标记的相关样本进行学习,用历次返回的结果更新训练样本集,建立SVM分类器模型,并根据模型进行检索。本文还对不同核函数的SVM进行了对比,得出RBF核函数的SVM有较高的检索精度。使用由10000幅图像组成的图像库进行实验,结果表明,该算法可有效地检索出更多的相关图像,并且在有限训练样本情况下具有良好的泛化能力。     

MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback

The field of Content-Based Visual Information Retrieval (CBVIR) has experienced tremendous growth in the recent years and many research groups are currently working on solutions to the problem of finding a desired image or video clip in a huge archive without resorting to metadata. This paper describes the ongoing development of a CBVIR system for image search and retrieval with relevance feedback capabilities. It supports browsing, query-by-example, and two different relevance feedback modes that allow users to refine their queries by indicating which images are good or bad at each iteration.     

基Web的图像搜索

介绍一种基于Web的图像搜索技术,提出了作者设计的图像检索的模型,阐述了Web页中图像与文本之间的关系,对相似度的计算、词条的匹配算法、检索的反馈技术等作了详细的论述,并通过实验检验搜索模型,结论表明图像搜索效率高。     

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,     

Self-Organising Maps as a Relevance Feedback Technique in Content-Based Image Retrieval

Self-Organising Maps (SOMs) can be used in implementing a powerful relevance feedback mechanism for Content-Based Image Retrieval (CBIR). This paper introduces the PicSOM CBIR system, and describes the use of SOMs as a relevance feedback technique in it. The technique is based on the SOM’s inherent property of topology-preserving mapping from a high-dimensional feature space to a two-dimensional grid of artificial neurons. On this grid similar images are mapped in nearby locations. As image similarity must, in unannotated databases, be based on low-level visual features, the similarity of images is dependent on the feature extraction scheme used. Therefore, in PicSOM there exists a separate tree-structured SOM for each different feature type. The incorporation of the relevance feedback and the combination of the outputs from the SOMs are performed as two successive processing steps. The proposed relevance feedback technique is described, analysed qualitatively, and visualised in the paper. Also, its performance is compared with a reference method.     

一种图像检索中的灰色相关反馈算法

在交互式CBIR系统中,由于用户的查询需求常常是模糊的,因此检索结果从某种意义上说是不确定的。于是,可以将图像检索过程视为一个“灰色系统”,其中的查询向量以及图像特征的权重可视为“灰数”。基于此,该文提出了一种新的相关反馈技术,它采用“灰关联分析”理论来分析和描述“例子图像”与“相关图像”之间的关系,据此自动更新查询向量与图像特征的权重,从而更准确地描述用户的查询需求。实验结果表明,这种相关反馈算法能较好地描述用户的查询需求,显著地改善了图像检索的性能。     

基于目标区域的图像综合特征检索

提出了一种基于目标区域的图像综合特征检索方法。根据颜色、纹理特征的综合向量采用改进的K均值聚类算法把每幅图像分成若干个区域,并且利用综合特征表示和检索分割后的区域,该方法在区域匹配时考虑了不同特征在重要程度上的差别。为了改善检索性能,系统中引入了快速索引和相关反馈技术。     

Colour Image Indexing Using SOM for Region-of-Interest Retrieval

In this paper, we present a novel approach to image indexing by incorporating a neural network model, Kohonen’s Self-Organising Map (SOM), for content-based image retrieval. The motivation stems from the idea of finding images by regarding users’ specifications or requirements imposed on the query, which has been ignored in most existing image retrieval systems. An important and unique aspect of our interactive scheme is to allow the user to select a Region-Of-Interest (ROI) from the sample image, and subsequent query concentrates on matching the regional colour features to find images containing similar regions as indicated by the user. The SOM algorithm is capable of adaptively partitioning each image into several homogeneous regions for representing and indexing the image. This is achieved by unsupervised clustering and classification of pixel-level features, called Local Neighbourhood Histograms (LNH), without a priori knowledge about the data distribution in the feature space. The indexes generated from the resultant prototypes of SOM learning demonstrate fairly good performance over an experimental image database, and therefore suggest the effectiveness and significant potential of our proposed indexing and retrieval strategy for application to content-based image retrieval. Receiveed: 4 June 1998?,Received in revised form: 7 January 1999?Accepted: 7 January 1999     

基于内容的自适应图象检索的设计

提出了一种自适应的图象检索机制。该机制将用户的高层语义信息有效地嵌入到了检索系统中,从而能精确地捕捉用户所需要的信息。     

基于内容的图像检索技术

随着大量数字图像数据库的出现,基于内容的图像检索技术成为研究热点。该文对目前基于内容的图像检索技术的主要原理与方法进行了分析,并对以后的发展趋势进行了展望。     

一种改进的基于标准离差的相关反馈图像检索方法

MARS系统是采用相关反馈方式进行图像检索的一个范型.本文在分析该系统所采用权值调整算法基础上,考虑负例图像因素引入了一个参数,并提出了一种新的改进算法.通过在由1000幅图像构成的Corel数据库中的试验,改进后算法在大部分图像类型的检索上性能有明显提升.     

Speed up interactive image retrieval

In multimedia retrieval, a query is typically interactively refined towards the “optimal” answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. Furthermore, it may also take too many iterations to get the “optimal” answers. In this paper, we introduce a new approach called OptRFS (optimizing relevance feedback search by query prediction) for iterative relevance feedback search. OptRFS aims to take users to view the “optimal” results as fast as possible. It optimizes relevance feedback search by both shortening the searching time during each iteration and reducing the number of iterations. OptRFS predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses (i.e., random accesses) can be saved, hence reducing the searching time for the next iteration. In addition, efficient scan on the overlap before the next search starts also tightens the search space with smaller pruning radius. As a step forward, OptRFS also predicts the “optimal” query, which corresponds to “optimal” answers, based on the early executed iterations’ queries. By doing so, some intermediate iterations can be saved, hence reducing the total number of iterations. By taking the correlations among the early executed iterations into consideration, OptRFS investigates linear regression, exponential smoothing and linear exponential smoothing to predict the next refined query so as to decide the overlap of candidates between two consecutive iterations. Considering the special features of relevance feedback, OptRFS further introduces adaptive linear exponential smoothing to self-adjust the parameters for more accurate prediction. We implemented OptRFS and our experimental study on real life data sets show that it can reduce the total cost of relevance feedback search significantly. Some interesting features of relevance feedback search are also discovered and discussed.