一种新的基于本体论描述的内容图像检索模型

分析了基于内容的图像检索中存在的问题,利用本体论方法建立图像底层特征本体及特定类图像本体.同时,定义了图像描述因子并建立相应的图像组合规则.最后,利用图像的底层特征进行图像检索,结合多分类支持向量机,实现图像底层特征与高层描述信息的关联,进而实现了图像语义检索,缩小了"语义鸿沟"对基于内容的图像检索的影响.实验结果表明本模型能够提高基于内容的图像检索的准确率,同时,经过3～5次反馈,可以实现语义检索功能.     

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.     

图像的语义关联与图像索引

基于内容图像检索的主要挑战在于不断变化的图像检索要求、难于表达的图像内容以及图像表达的数字阵列与通常可以被人类所接受的概念化内容之间的语义鸿沟.提出了一个基于语义关联的图像检索方法,在语义关联的基础上形成一个场景类别的语义表达,以便用户可以将感知上相似的图像组织在一起,形成概念上下文,使得用户可以解释和标记图像而无需给...     

一种基于记忆的图像检索相关反馈策略

相关反馈在基于内容的图像检索中成为提升检索效率的一项重要技术。然而,在图像检索中,高层语义与底层特征之间存在着巨大的"语义鸿沟",传统的相关反馈技术需要多次反馈才能获得满意结果,这使得用户的检索任务既耗时且繁琐。因此,本文通过对图像检索反馈日志信息的存储及使用过程进行分析,提出一种新的基于记忆的相关反馈策略。通过原型系统实验,与传统反馈策略相比,本文提出的策略对检索效率有明显改善。     

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。     

一种结合语义特征和视觉特征的图像检索方法

为了解决传统的CBIR系统中存在的"语义鸿沟"问题,提出一种结合语义特征和视觉特征的图像检索方法.将图像的语义特征和视觉特征数据结合到同一个索引向量中,进行基于内容的图像检索.系统使用潜在语义索引(LSI)技术提取图像的语义特征,提取颜色直方图作为图像的视觉特征.通过将图像底层视觉特征与图像在向量空间中的语义统计特征相...     

基于概念级语义的图像描述与识别

由于计算机自动提取的图像视觉特征与人所理解的图像内容存在巨大的差异,传统的低层的视觉特征(如颜色、纹理、形状等)CBIR(Content-Based Image Retrieval)系统的检索结果往往不尽如人意.近年来,根据概念级语义(如男孩、高兴、浪漫等)的CBIR引起了研究者的重视.本文对CBIR领域的大量文献进行了深入的分析,从工程角度综述了图像概念级语义的描述模型、概念级语义特征提取和概念级语义图像检索问题的研究进展,并阐述了作者的一些观点.     

结合相关反馈日志与语义网络的图像标注

连接高层语义和低层视觉特征的图像语义标注技术能够很好地表示图像的语义,提出并实现了一种结合相关反馈日志与语义网络的图像标注方法。该方法以收集的用户相关反馈日志为基础获得图像的语义信息,通过计算图像间的语义相似度进行语义聚类并采用语义传播的方式实现图像的语义标注。实验结果表明,随着相关反馈日志库的不断扩充,图像库中越来越多的图像会在反馈的过程中得到标注且标注的准确率会随着反馈次数的增加而趋于稳定。     

基于灰度-单元差分共生矩阵的医学图像的检索与分类

医学图像的检索与分类技术在计算机辅助诊断中具有重要作用.图像特征提取是基于内容的图像检索(CBIR)与分类中的关键技术之一.因此,如何有效地提取能够反映图像高层语义的低层特征对于医学图像的检索与分类是至关重要的.针对这个问题,提出使用灰度-单元差分共生矩阵提取纹理特征.在此基础上,使用欧氏距离和支持向量机(SVM)进行图像的检索与分类.实验结果表明,灰度-单元差分共生矩阵对于医学图像的检索与分类是有效的.     

采用形态学边界特征的医学图像检索

特征提取是基于内容的图像检索(CBIR)中的关键步骤,如何有效提取反映高层语义的图像特征对于医学图像的检索至关重要.提出一种基于边界形状特征的医学图像检索方法.该方法首先通过多尺度形态学方法检测图像边界点,然后对边界图像进行形状特征提取,构建边界的形状密度直方图,最后通过相似性匹配实现医学图像检索.实验结果证明了所提取的边界形状特征在医学图像检索中的有效性,通过对比实验给出了结果分析和进一步的研究思路.     

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.     

基于内容的图象检索技术

随着数字图象的日益增多,基于内容的图象检索已成为图象使用者和管理者迫切需要解决的问题,近年来,各国研究者纷纷加入该领域的研究.为了使人们对该领域现状有个概略了解,以推动该领域研究进一步开展,首先概括介绍了基于内容图象检索的产生、发展及其关键技术;然后介绍了特征提取(包括低层特征和语义特征)及其相似性计算、相关反馈等的原理及算法;最后指出了基于内容的图象检索技术与计算机视觉技术的区别所在,并对目前存在的问题和应着重的研究内容以及发展方向进行了分析.     

Hidden annotation for image retrieval with long-term relevance feedback learning

Hidden annotation (HA) is an important research issue in content-based image retrieval (CBIR). We propose to incorporate long-term relevance feedback (LRF) with HA to increase both efficiency and retrieval accuracy of CBIR systems. The work contains two parts. (1) Through LRF, a multi-layer semantic representation is built to automatically extract hidden semantic concepts underlying images. HA with these concepts alleviates the burden of manual annotation and avoids the ambiguity problem of keyword-based annotation. (2) For each learned concept, semi-supervised learning is incorporated to automatically select a small number of candidate images for annotators to annotate, which improves efficiency of HA.     

Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval.     

历史信息学习的相关反馈图像检索

相关反馈技术被有效的应用于基于内容的图像检索.传统的相关反馈未能充分利用检索的历史信息.为了进一步提高检索的效率与准确性,提出一种基于历史检索信息学习的相关反馈检索方法.该方法将每次检索的结果作为历史检索信息保存.进行新的检索时,判断当前查询图像与历史检索信息的语义相关性,预测检索结果,以期减少相关反馈次数.对包含80 00幅图像的图像库实验表明,与传统相关反馈技术相比,该方法明显的改善了检索性能.     

Image retrieval using nonlinear manifold embedding

The huge number of images on the Web gives rise to the content-based image retrieval (CBIR) as the text-based search techniques cannot cater to the needs of precisely retrieving Web images. However, CBIR comes with a fundamental flaw: the semantic gap between high-level semantic concepts and low-level visual features. Consequently, relevance feedback is introduced into CBIR to learn the subjective needs of users. However, in practical applications the limited number of user feedbacks is usually overwhelmed by the large number of dimensionalities of the visual feature space. To address this issue, a novel semi-supervised learning method for dimensionality reduction, namely kernel maximum margin projection (KMMP) is proposed in this paper based on our previous work of maximum margin projection (MMP). Unlike traditional dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA), which only see the global Euclidean structure, KMMP is designed for discovering the local manifold structure. After projecting the images into a lower dimensional subspace, KMMP significantly improves the performance of image retrieval. The experimental results on Corel image database demonstrate the effectiveness of our proposed nonlinear algorithm.     

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.