进化加速技术在图像检索中的应用

在感性的图像检索中采用交互式进化技术,可以将用户的直觉、情感等感性因素有机地融入进化过程中,但是存在无法有效解决用户易疲劳问题。针对这一不足,该文引入基于引导的进化加速算法,使得进化收敛的速度加快,从而来减轻用户疲劳,检索实验结果表明该算法在检索交互次数和检索效果上都有显著的改善。     

基于内容的图象检索中的语义处理方法

基于内容的图象检索系统,其目标是最大限度地减小图象简单视觉特征与用户检索丰富语义之间的“语义鸿沟”,因此图象语义处理则成为基于内容的图象检索进一步发展的关键。为了使人们对基于内容的图象检索中的语义处理方法有个概略了解,首先从图象语义模型和图象语义提取方法这两个方面对利用语义进行图象检索的研究状况进行了总结,并将图象语义模型概括为图象语义知识、图象语义层次模型和语义抽取模型等3个主要组成部分;然后将图象语义提取方法分为用户交互、将查询请求作为语义模板、对象及其空间关系、场景和行为语义及情感语义等类别,同时对其中有代表性的方法进行了详细的分析,还指出了其局限性;最后从对象建模和识别、语义抽取规则和用户检索模型3个方面,阐明了实现图象语义处理所面临的问题,并提出了一些初步的解决思路。     

基于感性的图像评估与检索

感性信息处理的主要研究目标之一是让计算机能够模拟和识别用户的偏爱、喜好等主观信息,适应不同用户的不同需求,实现以人为本.它的理论和应用研究刚刚起步,是一个全新的研究课题.本文基于R.Plutchik的情感理论,建立了四维的情感空间,提出了人工情感模型,并将其应用于图像的感性评估和检索;采用径向基神经网络建立图像特征空间和情感空间的联系,记忆用户的感性信息,对图像进行感性评估;采用交互式遗传算法,将用户的情感结合到图像检索过程,实现图像的感性检索取得了较好的实验结果.     

基于绝对尺度预测的交互式进化算法

预测用户的评估特性可以有效减轻交互式进化算法中的用户疲劳问题,但基于相对尺度的用户评估制约了预测的准确性.针对这一问题,本文提出一种基于绝对尺度预测的交互式进化算法,将用户的相对评估转化成绝对评估,减少预测器学习样本中的噪声,提高预测的准确性,从而加快算法的收敛速度,更好地减轻用户疲劳.文中采用6个标准函数模拟用户,验证算法的有效性.将该算法应用于服装图像的个性化情感检索,运用符号检验方法证实采用本文所提出的算法可以获得更好的检索结果.     

基于进化规划策略的纹理图象检索

由于进化规划具有群体搜索和随机信息交换的优点,可以对图象特征矢量进行优化选取,有鉴于此,本文提出了一种新的基于进化规划的纹理图象的检索方法,实验结果表明,该方法能够充分体现出进化规划算法在纹理图象检索方面的优越性,有效提高检索精度,改善了检索效果。     

基于IGA的用户Agent模型与设计

个性化信息检索与获取是目前理论与应用研究的一个热点.其解用户疲劳、加快算法的收敛.本文以图形检索为应用背景,提出了基于交互式遗传算法的用户Agent模型.该软件Agent针对现有研究的不足,将用户个性化信息获取与个性化检索集成在一起,两者相辅相成.在获取用户个性化信息时,我们设计了一种结合归纳和统计的用户情感计算机制,通过对前几代操作的结果进行归纳、计算,得到用户的特异性偏好;在利用用户情感偏好实现检索时,我们设计了利用个体偏好的引导进化方法来指导交互式遗传算法的选择、变异等操作.实验验证该模型在人脸图形检索中确实达到了体现用户个性化,有效缓解用户疲劳的目的.     

基于语义的图象检索方法研究综述

对于当前基于内容的图象检索（CBIR）系统来说,在用户的信息需求和千丝万缕诉功能之间存在着重大的差距,要进一步提高现有图象检索系统的能力,需要对图象的内容进行语义描述,对图象的多层语义模型和图象语义的知识表示作了概括性介绍,重点讨论了一些将图象的低层视觉特征映射到图象高怯事义的方法,并指出了利用语义进行图象检索急需解决的一些问题。     

基于树状小波分解的纹理图象检索

针对图象检索应具有简单、快速、有效等要求,提出了一种采用树状小波分解特征的纹理图象检索方法,该方法可以在相应的能量准则下,自适应地对图象进行了带分解,同时可利用小波函数分解的多分辨率与多方向特性,来形成能够在一定程度上对图象进行精确描述的特征矢量;在此基础上,又采用基于图象特征值的主分量分析方法,有效降低了特征矢量的维数;另外,基于用户需求的分层检索,还满足了用户不同层次的需求。实验结果表明,该算法快速,有效,具有较强的应用价值。     

多媒体系统中基于图象内容检索的特征指标设计

在多媒体系统应用中基于图象内容检索的特征指标设计问题。对图象和视频的基于内容的检索方法晃成功地开发一个多媒体数据库系统的关键,而对图象泊特征指标的提取则是关键中的关键。目前,基于内容检索的多媒体系统应用还外于初始阶段,并于图象特征指标的设计还没有统一的框架。本文应用图形处理学科中提出的多种指标来作为关于图象检索的线索,并提出了一个初步的检索构思。     

图象检索系统中的相关反馈技术研究

随着网络技术和通信技术的发展,图象和视频等数字媒体信息量正以指数形式增长.为了快速有效地检索这些数字信息,一种有效方法是采用基于内容的图象检索技术,但是,由于图象特征提取与描述、相似性度量方法等难以确定,使得查询描述无法嵌入用户语义信息,为此,设计了一个具有相关反馈的图象检索系统结构,并重点论述了基于修改查询矢量、基于神经网络及基于概率分布等几类相关反馈技术的思想及基本实现过程.通过比较分析表明,这几种相关反馈策略都可用于基于内容的查询.其中,修改查询矢量的相关反馈更适应于目标搜索,修改数据库概率分布的相关反馈更适应于随意浏览,而基于人工智能学习方法的相关反馈则更适应于分类搜索.     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture

In this paper, an unsupervised learning network is explored to incorporate a self-learning capability into image retrieval systems. Our proposal is a new attempt to automate recursive content-based image retrieval. The adoption of a self-organizing tree map (SOTM) is introduced, to minimize the user participation in an effort to automate interactive retrieval. The automatic learning mode has been applied to optimize the relevance feedback (RF) method and the single radial basis function-based RF method. In addition, a semiautomatic version is proposed to support retrieval with different user subjectivities. Image similarity is evaluated by a nonlinear model, which performs discrimination based on local analysis. Experimental results show robust and accurate performance by the proposed method, as compared with conventional noninteractive content-based image retrieval (CBIR) systems and user controlled interactive systems, when applied to image retrieval in compressed and uncompressed image databases.     

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。     

Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble

With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.     

Query-by-Gesture: An Alternative Content-Based Image Retrieval Query Scheme

Current research on content-based image retrieval (CBIR) is centered on designing efficient query schemes in order to provide a user with effective mechanisms for image database search. Among representative CBIR query schemes, query-by-sketch has been one of the attractive query tools that are highly adaptive to user's subjectivity. However, query-by-sketch has a few limitations. That is, most sketch tools demand expertise in image processing or computer vision of the user to provide good enough sketches that can be used as query. Furthermore, sketching the exact shape of an object using a mouse can be a burden on the user. To overcome some of the limitations associated with query-by-sketch, we propose a new query method for CBIR, query-by-gesture, that does not require sketches, thereby minimizing user interaction. In our system, the user does not need to use a mouse to make a sketch. Instead, the user draws the shape of the object that heshe intends to search in front of a camera by hand. In addition, our query-by-gesture technique uses relevance feedback to interactively improve retrieval performance and allow progressive refinement of query results according to the user's specification. The efficacy of our proposed method is validated using images from the Corel-Photo CD.     

发掘相关反馈日志中关联信息的图像检索方法

相关反馈日志蕴含着丰富的对象语义关联信息,但大多数基于内容的图像检索(CBIR)方法却缺乏对它们的重用.提出一种发掘反馈日志中图像关联信息的自动化图像检索方法,将反馈事例中图像的共生现象视为一定上下文中的图像分类.检索时,结合CBIR的检索结果和多种上下文中的图像分类实例,借鉴HITS算法的思想从中提炼图像的本质性关联,获得综合内容和语义的图像检索结果.对6万幅Corel图像数据库的实验表明,该方法可以显著改善查全率和查准率,且检索结果能够更好地满足用户的语义检索需求.     

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.     

基于小波变换的图像检索

随着多媒体和因特网技术的迅速发展,图像数据在不断增加,为了对这些图像进行更有效的管理和分析,帮助用户快速准确地找到所需内容的图像,基于内容的图像检索（CBIR）正成为当今多媒体技术研究的热点.本文采用基于小波变换的技术来提取图像的纹理特征,并使用支持向量机学习技术从图像数据库中检索出符合要求的图像,实验结果证明了所提出方法的有效性.     

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.