基于SVM与Adaboost的图像检索应用

语义图像检索已成为解决简单视觉特征和用户检索高级语义之间存在的"语义鸿沟"问题的关键,本文试图提出一种基于SVM和Adaboost集成学习相结合的相关反馈算法。在相关反馈过程中选择最具信息的样本训练支持向量机,可以有效减少相关反馈的次数和所需学习样本的数量,通过两者的互补来有效地提高图像检索的精度。最后提出Adaboost算法对SVM分类器进行加权投票,这样进一步提高了图像检索的性能。实验表明,该方法能较好地解决了图像检索中的小样本选择问题,并能显著提高图像检索的效率和性能。     

基于FSRM的相关反馈图像检索算法

在基于内容的图像检索中,低层视觉特征和高层语义之间的“语义鸿沟”一直是基于内容图像检索技术前进的一大障碍。相关反馈机制在一定程度上缩小了图像检索中的“语义鸿沟”。提出了一种基于模糊语义相关矩阵(FSRM)的相关反馈算法。该算法根据用户对检索结果的反馈调整模糊语义相关矩阵中的权值,从而捕捉用户的检索企图,通过对模糊语义相关矩阵中数据的学习不断修正语义矩阵,达到低层视觉特征到高层语义特征的过渡,最终提高了查询的准确度。实验结果证明了该算法的有效性。     

相关空间嵌入算法及其在图像检索中的应用

图像语义检索的一个有效解决途径是找到图像底层特征与文本语义之间的关联.文中在核方法和图拉普拉斯矩阵的基础上,提出一种相关空间嵌入算法,并利用文本隐性语义索引和图像特征的视觉单词,构造出文本语义空间与图像特征空间这两个异构空间的相关关系,从而找出文本语义与视觉单词间潜在关联,实现图像的语义检索.文中算法把保持数据流形结构的一致性作为一种先验约束,将文本语义空间和图像特征空间中的数据点嵌入到同一个相关空间中.因此,与典型相关分析算法相比,这种相关嵌入映射不仅可揭示不同数据空间之间存在的相关关系,还可在相关空间中保留原始数据分布结构,从而提高算法的可靠性.实验验证文中算法的有效性,为图像语义检索提供一种可行方法.     

基于保局投影的相关反馈算法

在原有保局投影算法中引入用户反馈,用其更新构建降维映射的特征向量,从而得到一个更能够反映语义属性的图像表示子空间.该算法利用用户反馈迅速优化图像表示,使它具有长期学习的能力.实验结果表明:该算法可以提高检索的准确度,而且在经过长期学习后可以获得一个近似最优的图像降维子空间.     

相关反馈中基于负例监督的特征过滤策略

相关反馈是提高检索精度和消除"语义鸿沟"的一种非常有效的方法.提出了一种新的特征过滤策略,该策略通过负例监督的方法构造一个特征过滤器,来选取正例样本独有的特征,然后采用一种基于最近邻分类思想的相似性度量方法对去除了不相关特征分量的候选图像进行排序.通过与传统相关反馈方法的比较,表明了该算法的高效性.     

基于选择关系嵌入算法的图像检索方法

结合流形学习和相关反馈技术的图像检索方法关键是结合低层可视化信息,从少量用户反馈信息中学习用户语义,以获得语义子空间流形。为获得更真实的语义子空间,文中在区分对待低层可视化和用户反馈信息的同时,基于低层可视化信息选择学习反馈信息中的类内和类间关系,提出一种选择关系嵌入算法应用于图像检索。该方法可保留更真实的语义流形结构,从而提高在低维空间中的检索精度。实验结果表明文中方法可将图像映射到更广范围的低维空间,在反馈迭代两次之后检索精度提高最高可达16。3%。     

基于半监督多示例学习的对象图像检索

针对基于对象的图像检索问题,提出一种新的半监督多示例学习(MIL)算法.该算法将图像当作包,分割区域的视觉特征当作包中的示例,按"点密度"最大原则,提取"视觉语义"构造投影空间;然后利用定义的非线性函数将包映射成投影空间中的一个点,以获得图像的"投影特征",并采用粗糙集(RS)方法对其进行属性约简;最后利用直推式支持向量机(TSVM)进行半监督的学习,得到分类器.实验结果表明,该方法有效且性能优于其他方法.     

利用蚁群算法的记忆式图像检索方法

如何有效利用用户的相关反馈信息来进行基于语义的图像检索,是一个具有重要意义并且极具挑战性的问题.介绍了一种基于蚁群算法的记忆式图像检索方法,它是传统记忆式图像检索方法的一种改进.用蚁群算法的思想,利用用户的反馈信息建立图像的语义网络,并依据该语义网络用迭代的方法来检索图像.实验表明,该方法不仅有效,而且存储量小、计算量...     

基于虚拟关联反馈与聚类的图像检索技术研究

基于虚拟相关反馈(PRF)技术,提出了一种新的自动关联反馈检索方法--外部自动相关反馈(OARF).该方法基于图像内容特征距离,应用K-均值聚类,自动扩展查询图像特征,从而提高检索性能.试验结果表明,OARF能够降低用户负担,显著提高原始检索算法的性能,缩小"语义鸿沟".     

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。     

HBIR: Hypercube-Based Image Retrieval

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval (CBIR) systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube (HC) in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the effective rules and suppress the inefficient ones. For improving the response time, an HC merging approach and, for reducing the conflicts, an HC splitting method is designed. Our experiments on a set of 11000 images from the Corel database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to some existing approaches reported recently in the literature. Moreover, our approach can be better trained and is not saturated in long time, i.e., any feedback improves the precision and recall of the system. Another strength of our method is its ability to address the dynamic nature of the image database such that it can follow the changes occurred instantaneously and permanently by adding and dropping images.     

An efficient semantic – Related image retrieval method

Many previous techniques were designed to retrieve semantic images in a certain neighborhood of the query image and thus bypassing the semantically related images in the whole feature space. Several recently methods were designed to retrieve semantically related images in the entire feature space but with low precision. In this paper, we propose a Semantic – Related Image Retrieval method (SRIR), which can retrieve semantic images spread in the entire feature space with high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.     

基于语义的自然图像检索及相关反馈算法研究

该文通过对图像进行分块,提取各子块颜色直方图构建出各图像的主颜色信息,建立语义矩阵,完成了图像的提取,通过相关反馈完成了图像的检索。实验表明,加入语义的图像检索速度显著提高,有效的缩减了高层语义和低层特征的鸿沟。     

一个基于语义的图像检索系统:VisEngine

介绍了一个基于语义的图像检索系统——VisEngine，该系统采用基于图像主要区域的图像分割方法，分别提取图像前景、背景和全局的视觉和抽象语义内容，构造相应的语义模板。接着把这些特征导入到一个面向对象的中间信息结构中，在此基础上进行多种方式的相似性匹配和检索。系统支持多种查询方式，用户交互界面自然友好。实验表明，VisEngine系统能有效地提高首次用户查询的正确性。     

基于语义学习的图像多模态检索

针对语义鸿沟问题,在语义学习的基础上设计图像的多模态检索系统。该系统结合3种查询方式进行图像检索。基于视觉特征的查询通过特征提取与相似度匹配进行排位。基于标签的查询建立在图像自动标注的基础上,但在语义空间之外的泛化能力较差。基于语义图例的查询能够在很大程度上克服这个缺陷,通过在显式或隐式的语义空间上进行查询,使检索结果更符合人类感知。实验结果表明,与基于纹理特征的图像检索相比,基于语义图例的检索具有更高的精度及召回率。     

基于多特征融合和相关反馈的图像检索

鉴于单一视觉特征不能很好地表达图像内容,提出一种融合图像颜色、形状、纹理特征的图像检索方法。最后采用支持向量机（SVM）的相关反馈算法提高图像检索的准确度,缩小低层特征和高层语义之间的语义鸿沟。实验结果说明提出的方法具有良好的检索性能。     

A new region filtering and region weighting approach to relevance feedback in content-based image retrieval

A new region filtering and region weighting method, which filters out unnecessary regions from images and learns region importance from the region size and the spatial location of regions in an image, is proposed based on region representations. It weights the regions optimally and improves the performance of the region-based retrieval system based on relevance feedback. Due to the semantic gap between the low level feature representation and the high level concept in a query image, semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters in the feature space. Our main goal is finding semantically related clusters and their weights to reduce this semantic gap. Experimental results demonstrate the efficiency and effectiveness of the proposed region filtering and weighting method in comparison with the area percentage method and region frequency weighted by inverse image frequency method, respectively.     

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.     

A survey of content-based image retrieval with high-level semantics

In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.