基于内容的视频／多媒体数据库检索技术

1.引言在当今的信息社会里,多媒体技术、计算机网络技术的迅速发展使得多媒体技术的应用也随之迅速发展。然而,可视化信息的大量生产和传播也带来一个问题.即面对信息量如此浩大的多媒体数据库,用户应如何找到自己需要的信息。例如:用户可能提出这样的要求,“找出数据库中所有赛车的场面”。我们需要研究的是,信息检索系统应如何适当表达这个要求的内容,并在数据库所有的视频信息中找到符合这个查询要求的信息返回给用户。于是,大规模多媒体数据库的索引和检索具有的巨大实用价值使得它在国外已成为一个重要的科研课题。     

基于视频时间段检索的多媒体数据库系统研究

本文在对当前视频检索的数据库系统进行分析的基础上,设计了基于时间段检索的多媒体数据库系统模型。该系统在关键帧提取时增加了对时间偏移量offset 的提取,进一步改进了目前视频建立索引的方法,提出了基于时间段的分层检索的概念,能为基于移动终端的短视频检索系统的研究与开发提供参考。     

多媒体数据基于内容的查询

分析了多媒体数据与传统数据之间的异同。将多媒体数据的属性分成“内容无关”、“导出”、“用户定义”３种类型,研究了多媒体对象之间的相似特点,最后给出了４种基本查询类型。     

一种通过视频片段进行视频检索的方法

视频片段检索是基于内容的视频检索的主要方式,它需要解决两个问题:(1) 从视频库里自动分割出与查询片段相似的多个片段;(2) 按照相似度从高到低排列这些相似片段.首次尝试运用图论的匹配理论来解决这两个问题.针对问题(1),把检索过程分为两个阶段:镜头检索和片段检索.在镜头检索阶段,利用相机运动信息,一个变化较大的镜头被划分为几个内容一致的子镜头,两个镜头的相似性通过对应子镜头的相似性计算得到;在片段检索阶段,通过考察相似镜头的连续性初步得到一个个相似片段,再运用最大匹配的Hungarian算法来确定真正的相似片段.针对问题(2),考虑了片段相似性判断的视觉、粒度、顺序和干扰因子,提出用最优匹配的Kuhn-Munkres算法和动态规划算法相结合,来解决片段相似度的度量问题.实验对比结果表明,所提出的方法在片段检索中可以取得更高的检索精度和更快的检索速度.     

基于内容检索的视频处理技术

从分析视频数据的结构和特点出发,总结了基于内容检索的视频处理方法的一般步骤,即视频分割、关键帧选取、静态和动态特征提取以及视频聚类等,然后深入介绍了各个处理过程中的一些最新方法,并分析了各种方法和技术的优缺点;最后,对基于内容的视频检索提出一些值得进一步研究的问题。     

Search Algorithms for Subdatatype-Based Multimedia Retrieval

Recently, researchers have mainly been interested only in the search for data content that are globally similar to the query and not in the search for inside data items. This paper presents an algorithm, called a generalized virtual node (GVN) algorithm, to search for data items where parts (subdatatype) are similar to the incoming query. We call this subdatatype-based multimedia retrieval. Each multimedia datatype, such as image and audio is represented in this paper as a k-dimensional signal in the spatio-temporal domain. A k-dimensional signal is transformed into characteristic features and these features are stored in a hierarchical multidimensional structure, called the k-tree. Each node on the k-tree contains partial content corresponding to the spatial and/or temporal positions in the data. The k-tree structure allows us to build a unified retrieval model for any types of multimedia data. It also eliminates unnecessary comparisons of cross-media querying. The experimental results of the use of the new GVN algorithm for subaudio and subimage retrievals show that it takes much less retrieval times than other earlier algorithms such as brute-force and the partial-matching algorithm, while the accuracy is acceptable.     

基于内容的视频检索

基于内容的视频检索一直是计算机科学研究的难点问题.从视频结构的分析入手,提出基于内容视频的研究问题、检索方式和评价指标,介绍当今国内外典型的基于内容的检索系统,对存在问题及发展趋势作出总结.     

基于内容的视频检索关键技术

基于内容的视频检索一直是计算机科学研究的难点问题,该文提出了它的研究问题、检索策略、检索种类和评价指标,指出了存在的问题及解决的方法。这些策略和方法作为北大方正媒体资产管理系统的核心功能,运用于电视台大型视频数据库的检索,取得了比较好的效果。     

A Language for Content-Based Video Retrieval

We present an effective technique for automatic extraction, representation, and classification of digital video, and a visual language for formulation of queries to access the semantic information contained in digital video. We have devised an algorithm that extracts motion information from a video sequence. This algorithm provides a low-cost extension to the motion compensation component of the MPEG compression algorithm. In this paper, we present a visual language called VEVA for querying multimedia information in general, and video semantic information in particular. Unlike many other proposals that concentrate on browsing the data, VEVA offers a complete set of capabilities for specifying relationships between the image components and formulating queries that search for objects, their motions and their other associated characteristics. VEVA has been shown to be very expressive in this context mainly due to the fact that many types of multimedia information are inherently visual in nature.     

Motion Flow-Based Video Retrieval

In this paper, we propose the use of motion vectors embedded in MPEG bitstreams to generate so-called ldquomotion flowsrdquo, which are applied to perform video retrieval. By using the motion vectors directly, we do not need to consider the shape of a moving object and its corresponding trajectory. Instead, we simply ldquolinkrdquo the local motion vectors across consecutive video frames to form motion flows, which are then recorded and stored in a video database. In the video retrieval phase, we propose a new matching strategy to execute the video retrieval task. Motions that do not belong to the mainstream motion flows are filtered out by our proposed algorithm. The retrieval process can be triggered by query-by-sketch or query-by-example. The experiment results show that our method is indeed superb in the video retrieval process.     

面向Web的新闻视频检索系统

本文针对人们通过网络点播新闻的需求,开发了面向Web的新闻视频检索系统NeWeb.该系统由Web服务系统及查询系统组成,前者与客户端交互,将客户端的检索需求传递给查询系统,并将结果以适当的形式返回给用户;后者负责组织新闻视频的内容并执行查询任务.NeWeb集成了基于内容的视频检索技术及Web技术,提高了检索效率,扩展了应用范围.     

镜头内容分析及其在视频检索中的应用

提出了一种镜头内容分析方法及其在视频检索中的两个应用:镜头检索与场景结构提取.为了刻画一个镜头的内容变化,首先引入两个新的内容描述子:主色直方图和空间结构直方图.主色直方图能够捕捉那些持续时间最长的颜色,而这些颜色是这段视频所关注的对象或背景的主要颜色.从颜色块图提取的空间结构直方图是描述图像空间信息的一组特征.一个变化较大的镜头可以划分为几个内容一致的子镜头,两个镜头的相似性可以从对应子镜头的相似性计算得到.镜头相似性度量可以直接用于镜头检索,还可用于场景结构提取.另外,还提出分裂与合并力量竞争的场景结构提取方法.在大容量视频数据库上进行实验所得结果证实了该方法在镜头检索和场景提取的优异表现.     

一种分级检索MPEG视频的方法

视频检索是当前的一个研究热点.以前的检索方法大多在像素域中进行,需要较大的解码运算量;且不加区分地对所有视频片断采用统一的匹配算法,浪费了许多不必要的计算.提出了一种基于样本的分级检索MPEG视频的新方法:首先用I帧的dct_dc_size字段快速粗检,然后用断层摄影(tomography)法分析B帧运动矢量的时空分布特性以进一步缩小结果集,最后用DC图像的精确匹配方法验证检索结果.试验结果表明,本方法所需计算量较小,且可保证较高的检索精度.     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.     

基于内容的视频检索技术

从分析基于内容的视频检索的优点和系统结构出发，重点探讨了系统中所涉及的镜头边界检测技术、关键帧提取技术、特征提取技术，分析了技术中各种方法的优缺点并介绍了一些新的方法，最后提出了一些值得进一步研究的问题。     

《微机原理与接口技术》多媒体软件设计与制件

论述了开发《微机原理和接口技术》多媒体CAI教学软件的设计思想、讨论了利用VisualBasic开发本软件的特点和方法。针对应用中所涉及到的进程条拖动、超文本、超图像、动画与声音的同步等多项技术的实现问题，提出了解决的方案。     

数字图书馆科技文献知识导航

提出了一种基于分类法和主题词表的科技文献知识导航体系,该体系支持分类法和主题词表知识导航、元数据结构查询和全文检索这三种检索手段以及他们的混合应用。从分类法主题词表的概念浏览和元数据查询的语义支持的角度来说,这是一个支持概念检索的知识导航体系。根据该体系,实现了“北京大学科技文献检索系统”实验数字图书馆。     

语音导航系统中的一种模糊检索算法

基于车载的语音导航系统,尤其是针对大地名庠中地名识别的任务需求.用LVCSR取代了较流行的基于语法的语音识别系统,以增强系统灵活性和处理能力.用一套模糊检索算法作为系统的后处理,提高地名识别的整句正确率.在普通的数万量级的地名数据库中可以达到90%的地名识别正确率;即使增加到百万数量级,识别率也能保持在70%以上.目前支持中英文系统.     

基于内容的视频检索研究

基于内容的检索能使用户根据媒体特征对媒体内容进行检索和查询．由于多媒体数据中含有丰富的视频数据，并且是随时间动态变化的其特征很难用一般的静态特征来描述，为了取得视频数据的特征．对视频数据的处理非常重要，本文将介绍基于内容的视频检索中相似索引的处理技术和方法。