电子商务中的基于内容的图像检索研究

CBR(基于内容的检索)是按照图像或视频等对象的自身特征去搜索,与传统上先对多媒体对象进行文本描述,搜索时采用关键字匹配的方式是不同的。MPEG-7标准是提供多媒体信息的标准化描述,以实现快速有效的搜索。CBR和MPEG-7的发展为商务网站的搜索设计提供了新的模式,可以为用户提供基于图像或视频内容的检索方式,方便用户的查询。     

电子商务中的基于内容的图像检索研究

CBR(基于内容的检索)是按照图像或视频等对象的自身特征去搜索,与传统上先对多媒体对象进行文本描述,搜索时采用关键字匹配的方式是不同的.MPEG-7标准是提供多媒体信息的标准化描述,以实现快速有效的搜索.CBR和MPEG-7的发展为商务网站的搜索设计提供了新的模式,可以为用户提供基于图像或视频内容的检索方式,方便用户的查询.     

基于内容的视频查询系统研究

由于多媒体数据库管理和检索的效率直接决定了人们利用多媒体数据信息的效率,因此随着MPEG-7标准的提出,基于内容的图象/视频存储和检索已成为研究的热点.为了快速地对视频进行浏览和检索,在研究基于内容的视频数据库管理和检索等热点问题的基础上,首先使用MPEG-7视觉内容描述子和语义描述子来构建视频数据库的语义结构,并结合底层视觉特征和高层语义特征,采用相关反馈机制和半自动权重更新体制来对视频数据库进行管理和检索;然后采用语法分析器来支持自然语言查询;最后在此基础上实现了基于内容的视频数据库的管理和查询系统.实验证明,该系统能够有效地对视频数据进行管理和检索,并且具有一定的智能性和适应性.     

DCD用于相关反馈的自然图像检索

基于内容的图像检索是多媒体应用研究领域的一项关键技术.传统的CBIR系统一般采用基于图像的低级物理特征进行特征匹配,MPEG-7标准的提出为图像内容特征的提取与描述提供了一种新型简便的方法,本文讨论了应用MPEG-7的主体颜色描述符DCD对图像特征提取和表示的方法,并在图像检索中的应用研究基础之上,提出了在使用相关反馈的图像检索中的改进方案,实现了基于DCD特征描述的自然图像检索.对于1350幅的数字图象组成的数据集进行了实验,并对实验结果进行分析,大部分结果是令人满意的,提高了基于内容的图像检索的精度.     

基于Bayes算法实现图像语义分类

介绍了根据图像的逻辑特征和抽象属性进行检索的基于语义分类的图像检索技术,并用Bayes分类算法设计了一个语义分类器,该语义分类器通过计算用户要查询图像的后验概率,对被查询的图像进行语义分类。     

基于小波变换和支持向量机的音频分类

音频特征提取是音频分类的基础,而音频分类又是内容的音频检索的关键。综合分析了语音和音乐的区别性特征,提出一种基于小波变换和支持向量机的音频特征提取和分类的方法,用于纯语音、音乐、带背景音乐的语音以及环境音的分类,并且评估了新特征集合在SVM分类器上的分类效果。实验结果表明,提出的音频特征有效、合理,分类性能较好。     

多媒体对象的组织与结构化检索

多媒体的结构化检索具有广泛的应用前景，但多媒体的检索技术尚不成熟。多媒体数据模型的复杂性，连续媒体基于内容检索的低效，以及缺乏适用的查询语言，都使得多媒体检索困难重重。英国肯特大学新近研制的多媒体检索系统在多媒体检索的相关领域取得了一定的突破。这个系统把用户视图中的多媒体对象组织成具有层次结构的虚拟数据库，使用属性来标识数据库中多媒体对象索引的特征；查询代理机允许用户直观地构造查询过程－包括一个结     

基于内容的音频检索与分类

音频信息检索技术是多媒体检索技术中的一个重要组成部分。该文在分析国内外现行的音频信息检索方法的基础上,总结出了基于内容的音频检索的一般方法,提出了一种基于内容的音频信息检索与分类方法,并通过已研制的ARS音频检索系统进行了性能测试。此方法具有较高的准确性和灵活性,可适用于不同的系统和应用。     

多媒体信息检索技术研究应用

在网络和多媒体技术快速发展的今天,如何在众多以图像、音频、视频等信息为代表的多媒体数据中有效的对其进行组织、存取、查询、提取是当前面临的一个重要问题。通过分析目前的多媒体信息检索技术,介绍了用于多媒体内容描述的国际标准MPEG-7;从图像、音频、视频三方面讨论当前广泛使用的一些信息检索技术并对这些技术的实际应用作了分析;最后,给出了一个视频应用模型。     

基于内容的音频检索技术综述

基于内容的音频检索是多媒体检索技术中一个重要的组成部分,而其检索技术却相对滞后.基于内容的音频检索已成为多媒体检索技术的研究热点.本文分析并总结了音频检索的概念,综述了基于内容的音频检索方法和相关技术,最后通过一个简单的系统对基于内容的音频检索方法进行了测试.     

一种MPEG-7视频语义摘要系统

该文设计并且实现了一个应用于E-Learning领域的视频语义摘要系统，它包括一个兼容MPEG-7标准的标注子系统、搜索引擎子系统和一个内容整合子系统；它专注于E-Learning领域，不但可以在多媒体的后期处理中添加描述，而且能实时录制／自动实时描述和辅助以语音参考描述。     

Content based audio classification: a neural network approach

Content based music genre classification is a key component for next generation multimedia search agents. This paper introduces an audio classification technique based on audio content analysis. Artificial Neural Networks (ANNs), specifically multi-layered perceptrons (MLPs) are implemented to perform the classification task. Windowed audio files of finite length are analyzed to generate multiple feature sets which are used as input vectors to a parallel neural architecture that performs the classification. This paper examines a combination of linear predictive coding (LPC), mel frequency cepstrum coefficients (MFCCs), Haar Wavelet, Daubechies Wavelet and Symlet coefficients as feature sets for the proposed audio classifier. Parallel to MLP, a Gaussian radial basis function (GRBF) based ANN is also implemented and analyzed. The obtained prediction accuracy of 87.3% in determining the audio genres claims the efficiency of the proposed architecture. The ANN prediction values are processed by a rule based inference engine (IE) that presents the final decision.     

一种反Web Spam页面的方法

最近,spam页面急剧增加,这极大的影响了搜索引擎的精度和效率。如何抵御spam页面已经成为一个非常重要的问题。文章合并了基于内容来侦测spam页面和基于链接spam侦测spam页面的方法,从而提出了一个两步的侦测spam页面的方法。第一步是一个过滤的步骤,用于生成spam页面的候选列表;第二步,通过一个自动的分类器从候选页面中侦测出最终的spam页面。     

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.     

Detection of cloaked web spam by using tag-based methods

Web spam attempts to influence search engine ranking algorithm in order to boost the rankings of specific web pages in search engine results. Cloaking is a widely adopted technique of concealing web spam by replying different content to search engines’ crawlers from that displayed in a web browser. Previous work on cloaking detection is mainly based on the differences in terms and/or links between multiple copies of a URL retrieved from web browser and search engine crawler perspectives. This work presents three methods of using difference in tags to determine whether a URL is cloaked. Since the tags of a web page generally do not change as frequently and significantly as the terms and links of the web page, tag-based cloaking detection methods can work more effectively than the term- or link-based methods. The proposed methods are tested with a dataset of URLs covering short-, medium- and long-term users’ interest. Experimental results indicate that the tag-based methods outperform term- or link-based methods in both precision and recall. Moreover, a Weka J4.8 classifier using a combination of term and tag features yields an accuracy rate of 90.48%.     

企业智能化Web商务数据挖掘引擎算法设计与实现

针对现有的数据挖掘引擎难以满足企业不断增长的需求的问题,在传统搜索引擎的基础上提出了一种企业专用的智能化Web商务数据挖掘引擎.从总体上给出了模型框架,得出企业智能化Web商务数据挖掘引擎是一个基于多模型的系统的结论.接着分别详细研究了该引擎的:Web内容挖掘、Web信息获取、客户行为挖掘3个核心模块.并给出了3个模块的详细算法设计和实现过程     

WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.     

一种中文网页自动分类方法的实现及应用

为了有效地组织和分析海量的Web信息，文章应用有指导的机器学习方法实现了一个中文网页分类器，并应用该分类器在“天网”搜索引擎上实现了大规模中文网页的目录导航服务。实验结果表明该分类器有较高的分类质量，满足了中文网页自动分类的要求。     

A Survey of MPEG-1 Audio,Video and Semantic Analysis Techniques

Digital audio & video data have become an integral part of multimedia information systems. To reduce storage and bandwidth requirements, they are commonly stored in a compressed format, such as MPEG-1. Increasing amounts of MPEG encoded audio and video documents are available online and in proprietary collections. In order to effectively utilise them, we need tools and techniques to automatically analyse, segment, and classify MPEG video content. Several techniques have been developed both in the audio and visual domain to analyse videos. This paper presents a survey of audio and visual analysis techniques on MPEG-1 encoded media that are useful in supporting a variety of video applications. Although audio and visual feature analyses have been carried out extensively, they become useful to applications only when they convey a semantic meaning of the video content. Therefore, we also present a survey of works that provide semantic analysis on MPEG-1 encoded videos.     

覆盖算法下文本分类特征选择的研究

文本分类是信息检索和数据挖掘的基础,被广泛应用于网络数据挖掘及搜索引擎等方面。首先对文本进行分词,对分词的结果分别使用x2统计量(CHI)方法与相关系数法(CC法)进行降维,并使用维数调节的思想进行特征提取。在得到特征集后,使用覆盖算法作为文本分类器进行学习。实验结果表明,通过结合相关系数法、覆盖算法以及维数调节方法,可实现一个效果较好的文本分类器。