一种基于粗糙集的相关反馈图像检索方法

针对如何在图像检索系统中客观地表达用户的感知，提出了一种基于粗糙集理论的相关反馈算法。通过相关反馈过程将用户感知与图像特征相结合，利用粗糙集理论归纳用户感兴趣的图像语义特征，并根据用户感兴趣的程度调整对应图像特征权重。作者建立了一个实验系统ISS，采用颜色直方图与语义特征作为图像特征，并实现MARS的反馈算法作为性能比较算法。实验结果表明，该算法较MARS系统在检索性能上有较大的提高。     

基于灰色系统理论的多特征相关反馈图像检索

探讨了灰色系统理论在相关反馈图像检索中的应用，提出了一种基于灰色系统理论的相关反馈图像检索算法，动态地更新查询向量，从而使图像检索结果与人的主观感知更加接近，具有自适应性。实验结果表明，文中的方法是很有效的。     

查询子空间在图像检索中的应用

近几年来,为了解决图像检索系统中由底层视觉特征和高层语义之间的差异所造成的检索困难,从信息捡索中引入了相关反馈技术。在过去几年中,它在该研究领域取得了一定的成功。文章提出了一种利用反馈信息建立“查询子空间”的检索模型,它将用户的语义查询进行基于视觉特征的分类,构造多个“查询子空间”,这些子空间拥有自身的查询模型和检索模型,最后的检索结果根据这多个“查询子空间”的检索结果得到。该模型具有较强的灵活性、扩展性,有效地利用了用户的反馈信息,动态地建立了底层视觉特征和高层语义之间的映射,能适应不同用户的查询。同时,将负反馈信息合理地融入到该模型中,提高了系统的检索效率。实验结果表明采用该检索模型的系统相比现有的检索系统性能有了较大提高。     

一种基于多分类SVM的相关反馈图像检索方法

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,SVM因其通用性和出色的分类能力逐渐被引入到图像检索系统中.为了进一步提高检索效率,采用三级反馈机制引入模糊相关,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,并对经典的查询向量点移动算法进行修改,在此基础上运用多分类SVM提出一种新的相关反馈图像检索方法.试验表明这是一个有效的方法.提高了图像检索效率.     

一种改进的粒子群算法和相关反馈的图像检索

由于图像的低层特征与高层语义之间存在着语义鸿沟,以及用户对图像理解的主观性和易变性,使得基于内 容的图像检索结果不能很好地满足用户的需求。为解决这个问题,将粒子群算法和相关反馈引入到图像检索过程中, 根据用户的反馈信息,引入二自适应调整和Beta自适应变异的粒子群算法动态调整图像的特征权重,从而提高图像 的检索精度,以更好地满足用户的需求。     

基于动态权重查询点移动的正相关反馈方法

为克服查询点移动算法中各加权常数的不确定性,以及权重查询点移动方法中正反馈样本构成的衰减向量非最优化问题,提出了一种新的正相关反馈算法.在确定最优查询向量时,以记忆的前次最优查询向量作为以往正反馈的衰减向量,利用当次用户所选反馈样本动态地确定衰减因子,最后将衰减向量与本次用户所选反馈样本结合不断更新最优查询向量,为后续反馈积累了更多用户检索意图信息.对比实验结果表明,该算法能加快最优查询向量调整速度,改善检索性能.     

一种改进的粒子群算法和相关反馈的图像检索

由于图像的低层特征与高层语义之间存在着语义鸿沟,以及用户对图像理解的主观性和易变性,使得基于内容的图像检索结果不能很好地满足用户的需求.为解决这个问题,将粒子群算法和相关反馈引入到图像检索过程中,根据用户的反馈信息,引入w自适应调整和Beta自适应变异的粒子群算法动态调整图像的特征权重,从而提高图像的检索精度,以更好地满足用户的需求.     

遗传反馈的多特征图像检索

基于内容的图像检索是随着数字多媒体技术的发展和普及而新兴的一门信息检索技术。针对当前该领域存在的对图像描述不准确、查询精度低以及反馈次数较多的问题,提出一种基于遗传反馈的图像检索算法。该算法以遗传算法和相关反馈为基础,利用多特征进行检索,避免在利用单一特征进行检索时所出现的不同图像具有相同单一特征（颜色、纹理和形状等）的问题,对图像进行多特征描述可以从多个角度对图像进行定义,大大减少了不同图像却具有相同特征的概率。与现有的算法相比,其具有自动调整图像特征权重、较低反馈次数和较高查询精度的特性。实验结果表明,该算法对于旋转、平移和尺度变化具有较强的鲁棒性,同时具有减少反馈次数和较高查询精度的性能。     

基于草图查询的语义对象图像检索

针对图像低层特征和高层语义之间存在的语义鸿沟问题,提出一种结合草图查询和相关反馈的语义对象图像检索系统.该系统首先根据用户输入的手绘草图,利用形状(轮廓、区域和骨架)特征从对象库中初步检索出语义对象并保存其区域组合;然后根据用户选择的反馈对象并结合查询草图提取用户检索对象的语义特征(形状、区域及拓扑特征),最后采用寻找最优区域配对的方法在系统特征库中进行检索.实验结果表明,本文方法不但对用户需求的语义对象有较好的检索效果,而且还能较准确地在结果图像中框选出用户感兴趣的语义对象.     

基于相关反馈的图像标注与检索算法

为了提高图像标注与检索的性能,提出了一种基于区域分割与相关反馈的图像标注与检索算法。该算法利用视觉特征与标注信息的相关性,采用基于区域的视觉特征对每幅图像采用聚类方法获得其一组视觉相似图像。通过计算与其距离最近的前3个分类的相似度,然后对这些关键字概率向量进行整合,获得最适合该图像的关键字概率向量,对图像进行标注。利用用户的反馈信息,修正查询关键词与每个分类之间的关系,进一步提高图像检索的准确性。实验结果表明,提出的算法具有更高的查准率与查全率。     

基于曲波的纹理图像检索系统的设计与实现

为了管理和查询海量图像,迫切需要一个基于内容的高检索率的图像检索系统。本文提出了一种以曲波变换为基础,综合香农熵与频域子带能量特征的图像检索算法。该方法用香农熵进行预分类,用子带图像的能量特征进行相似度度量,并加入检索者的反馈信息,实现图像的精确检索。用于Brodatz纹理图像库的检索实验结果表明,该系统有高的检索率和一定的实用价值。     

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。     

基于内容过滤的个性化搜索算法

传统信息检索技术满足了人们一定的需要,但由于其通用的性质,仍然不能满足不同背景、不同目的和不同时期的查询请求.提出了一种基于内容过滤的个性化搜索算法.利用领域分类模型上的概率分布表达了用户的兴趣模型,然后给出了相似性计算和用户兴趣模型更新的方法.对比实验表明,概率模型比矢量空间模型更好地表达了用户的兴趣和变化.     

基于加权关系图谱特征的图像检索

基于相似性度量的图像检索方法大多仅考虑检索图像与结果图像之间的距离，而不考虑结果之间的关系，使得检索精度受到影响。该文提出了基于加权关系图谱特征的图像检索算法，该算法利用检索图像与检索初始结果图像的距离构造加权关系图，利用该关系图的谱系数夹角特征确定最终输出的检索结果。对比检索实验表明，该方法可以提高检索的精度、具有较好的稳定性。     

An interactive approach for CBIR using a network of radial basis functions

An important requirement for constructing effective content-based image retrieval (CBIR) systems is accurate characterization of visual information. Conventional nonadaptive models, which are usually adopted for this task in simple CBIR systems, do not adequately capture all aspects of the characteristics of the human visual system. An effective way of addressing this problem is to adopt a "human-computer" interactive approach, where the users directly teach the system about what they regard as being significant image features and their own notions of image similarity. We propose a machine learning approach for this task, which allows users to directly modify query characteristics by specifying their attributes in the form of training examples. Specifically, we apply a radial-basis function (RBF) network for implementing an adaptive metric which progressively models the notion of image similarity through continual relevance feedback from users. Experimental results show that the proposed methods not only outperform conventional CBIR systems in terms of both accuracy and robustness, but also previously proposed interactive systems.     

An Architecture for and Query Processing in Distributed Content-based Image Retrieval

Images are being generated at an ever increasing rate by diverse military and civilian sources. A content-based image retrieval system is required to utilize information from the image repositories effectively. Content-based retrieval is characterized by several generic query classes. With the existence of the information superhighway, image repositories are evolving in a decentralized fashion on the Internet. This necessitates network transparent distributed access in addition to the content-based retrieval capability.Images stored in low-level formats such as vector and raster are referred to as physical images. Constructing interactive responses to user queries using physical images is not practical and robust. To overcome this problem, we introduce the notion of logical features and describe various features to enable content-based query processing in a distributed environment. We describe a tool named SemCap for extracting the logical features semi-automatically. We also propose an architecture and an application level communication protocol for distributed content-based retrieval. We describe the prototype implementation of the architecture and demonstrate its versatility on two distributed image collections.     

Filter Image Browsing: Interactive Image Retrieval by Using Database Overviews

Human-computer interaction is a decisive factor in effective content-based access to large image repositories. In current image retrieval systems the user refines his query by selecting example images from a relevance ranking. Since the top ranked images are all similar, user feedback often results in rearrangement of the presented images only.For better incorporation of user interaction in the retrieval process, we have developed the Filter Image Browsing method. It also uses feedback through image selection. However, it is based on differences between images rather than similarities. Filter Image Browsing presents overviews of relevant parts of the database to users. Through interaction users then zoom in on parts of the image collection. By repeatedly limiting the information space, the user quickly ends up with a small amount of relevant images. The method can easily be extended for the retrieval of multimedia objects.For evaluation of the Filter Image Browsing retrieval concept, a user simulation is applied to a pictorial database containing 10,000 images acquired from the World Wide Web by a search robot. The simulation incorporates uncertainty in the definition of the information need by users. Results show Filter Image Browsing outperforms plain interactive similarity ranking in required effort from the user. Also, the method produces predictable results for retrieval sessions, so that the user quickly knows if a successful session is possible at all. Furthermore, the simulations show the overview techniques are suited for applications such as hand-held devices where screen space is limited.     

Content-based image retrieval based on a fuzzy approach

A typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how fuzzy set theory can be effectively used for this purpose and describe an image retrieval system called FIRST (fuzzy image retrieval system) which incorporates many of these ideas. FIRST can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. FIRST uses fuzzy attributed relational graphs (FARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-complexity fuzzy graph matching algorithm is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorithm avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures.     

基于内容的图像检索系统性能评价

在图像检索需求多元化和专业化的推动下,CBIR技术日趋成熟,目前已有越来越多的商用和科研系统相继推出。这样就迫切需要展开对CBIR系统性能评价标准的研究,因为任何一项技术都是由该领域中相应的评价标准来推动的。为了使人们对这方面的现状动态有一概略了解,首先讨论了基于内容的图像检索评价过程中的两个基本问题,即大规模数据库的建立和获取进行相关性评判,然后对近年来文献中所见的基于内容的图像检索系统性能评价方法进行了回顾和综述;最后在此基础上,对其发展方向进行了探讨,并提出建立一个标准的测试数据集用来推动基于内容的图像检索系统性能评价的发展和更好地将人结合到基于内容的图像检索系统的性能评价过程中,以发展交互式的性能评价方法的建议。