Information retrieval and hypertext: competing technologies or complementary access methods

Abstract. Information retrieval typically involves accessing textual information from a database in response to a user's vague information need. Hypertext or hypermedia, on the other hand, involves a user browsing through a database of textual or multimedia information in response to a variety of types of information need. Thus information retrieval can be said to have a searching metaphor while hypertext has a browsing analogy. Initially, these two technologies for information access appear to be very different, almost competitive in nature. In this paper information retrieval systems are briefly reviewed and hypertext systems are also examined. These two techniques for accessing information have been integrated into a prototype system which is described. The system dynamically generates guided tours in response to a user's query and the tour guides the user through the hypertext. Some experiments reporting on the effectiveness of this as an information access strategy are given.     

Rich Get Richer——图像检索中的一种自适应的相关反馈方法

早期的基于内容的图像检索系统以图像处理技术为核心,研究重点集中在视觉特征的选择和提取方面,而没有充分利用人们在视觉方面的主观性和人类所广泛使用的高层次概念和低层次视觉特征之间的相关性。为解决上述问题,近年来相关反馈在基于内容的图像检索中受到重视。提出了一种新的相关反馈方法,使得高层次语义特征能够逐步嵌入到基于 低层次特征的图像检索中,该方法不仅能够记忆以前的交互信息,而且能够记忆相应的交互信息给系统带来的影响,实验结果表明该方法准确率高、响应速度快。     

图像检索中的动态相似性度量方法

为提高图像检索的效率,近年来相关反馈机制被引入到了基于内容的图像检索领域。该文提出了一种新的相关反馈方法－－动态相似性度量方法。该方法建立在目前被广泛采用的图像相拟性度量方法的基础上,结合了相关反馈图像检索系统的时序特性,通过捕获用户的交互信息,动态地修正图像的相似性度量公式,从而把用户模型嵌入到了图像检索系统,在某种程度上使图像检索结果与人的主观感知更加接近。实验结果表明该方法的性能明显优于其它图像检索系统所采用的方法。     

基于神经网络自学习的图像检索方法

相关反馈技术是近年来图像检索中较为活跃的研究方法之一.提出了一种基于神经网络自学习的图像检索方法,即在检索阶段利用人-机交互技术选出与检索图像相似的正例样本,然后构造出前向神经网络,进行自学习,以逐步达到提高查询效果的目的.神经网络的构造过程即是学习的过程,而且可以不断地学习.使用由9918幅图像组成的图像库进行实验,结果表明,该方法有助于用户表达查询意图和语义概念,可以通过交互式检索逐步求精地查找出更多、更准确的图像,并且具有较强的鲁棒性,可以结合各种特征表示和相似性匹配方法,交互地提高检索性能.     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

基于相关文档池建模的查询扩展

在信息检索领域,相关反馈是提高检索性能的有效方法之一。所谓相关反馈,指用户按照一定策略从查找到的相关文档中选择一些和主题相关的词进行查询扩展的技术。本文介绍了概率模型和向量空间模型下的常用查询扩展方法,并提出了一种基于语言模型的相关反馈方法,该方法同时考虑了扩展词应该具备的两个特征,即相关性和覆盖性。在TREC测试集上对这些算法进行了比较,结果表明这种新算法在平均准确率上比传统方法有所提高。     

基于查询行为和关联规则的相关反馈查询扩展

针对现有查询扩展缺陷,提出基于用户查询行为和词间完全加权关联规则挖掘的相关反馈查询扩展算法。在不改变用户查询信息习惯的前提下,无须用户参与,根据用户查询行为判断初检文档的相关性,提取相关的初检文档,挖掘与原查询相关的关联规则,构造规则库,从中提取与原查询相关的扩展词,实现查询扩展。实验结果表明,该算法能提高信息检索性能,具有很好的应用前景。     

基于受限随机选择和检索结果集的相关反馈

本文将相关反馈看成一个二分类问题,从反馈中固有的问题和用户的需求为出发点,在传统的移动查询点的基础上,提出了改进的基于受限随机选择和检索结果集的图像检索相关反馈系统。实验证明,该方法很好的解决了反馈中固有的小样本问题和正负样本不对称的问题。同时,综合利用用户的反馈信息,在较少的反馈次数内得到了较好的检索准确率。     

Instance based personalized multi-form image browsing and retrieval

It is important to adapt and personalize image browsing and retrieval systems based on users’ preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users’ preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme.     

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。     

基于内容的大数据量商标检索系统

到目前为止，大数据量的图像检索依然是一个难题，提出了运行在一种大型数据库上的基于内容的快速的商标图像检索．首先，从商标图像中提取两种统计特征，然后采用概率主成分分析降维，生成特征字典一数据库中商标图像集的一个特征映射．在检索阶段，采用快速的层次检索来得到一个数目不定的候选集，再通过相关反馈进行不断的优化，将候选集的数目减少．直至符合检索要求．在国家商标局提供的30，0270商标图像上运行本系统，每一个查询时间不超过0．3秒．     

基于RBFN的交互式图像检索方法

相关反馈技术是近年来图像检索中的重要研究方向,它有效地缩短了用户高层语义和图像底层视觉特征的差距,大大提高了系统的检索精度。文中从机器学习的角度出发,提出了一种基于RBFN的相关反馈算法。同时,为了方便用户对检索结果的标记,将模糊逻辑引入到图像检索中。即:用户对检索结果标记为相关图像、模糊相关图像和不相关图像,利用这些反馈信息动态地建立RBFN的结构,并进行检索,这个过程反复进行直到用户得到满意的结果。实验表明,这种方法在图像检索中具有更好的性能和更强的推广能力。     

A new query reweighting method for document retrieval based on genetic algorithms

In this paper, we present a new method for query reweighting to deal with document retrieval. The proposed method uses genetic algorithms to reweight a user's query vector, based on the user's relevance feedback, to improve the performance of document retrieval systems. It encodes a user's query vector into chromosomes and searches for the optimal weights of query terms for retrieving documents by genetic algorithms. After the best chromosome is found, the proposed method decodes the chromosome into the user's query vector for dealing with document retrieval. The proposed query reweighting method can find the best weights of query terms in the user's query vector, based on the user's relevance feedback. It can increase the precision rate and the recall rate of the document retrieval system for dealing with document retrieval.     

Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback

Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.     

Multimodal Retrieval using Mutual Information based Textual Query Reformulation

Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval.     

基于索引和相关反馈的图像检索研究

在基于图像内容的图像检索(CBIR)系统中,搜索引擎检索图像类似于按照相似标准来查询图像,它应该有足够快的速度并且有较高的检索准确率.索引用来提高系统响应,而相关反馈用于帮助提高检索准确率.在本文中,主要说明基于人感知的相似性度量,以及讨论综合相关反馈的索引方案.该索引方案通过分析特征熵而得出的主从键,而相关反馈是根据Mann-Whitnev检验而提出的,该检验通常用来识别来自同一搜索集中相关图像和不相关图像之间不同特征,并利用不同特征的特点提高检索性能.相关反馈方案针对两不同相似标准来执行,检验判定了这个方法的有效性.最后,把索引机制和相关反馈机制结合起来建立搜索引擎.     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

基于前向神经网络的图像检索相关反馈算法设计

相关反馈技术是近年来较为重要的图像检索方法，该文从机器学习的角度出发，提出了一种基于神经网络的相关反馈算法，在检索过程中，用户可以标记出与查询图像相似的正例样本反馈给系统，然后由系统构造出前向神经网络并再次进行检索，以改进查询结果。该文对用于训练前向神经网络的构造性算法在图像检索中的几何意义进行了深入的研究，并在此基础上给出平移算法、反例标记学习算法、球半径系数自适应算法等，从而使基于神经网络自学习的相关反馈算法更加完善。实验表明，改进后的算法在图像检索中具有更好的性能和更强的推广能力。     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.