基于综合推理的多媒体语义挖掘和跨媒体检索

为了更准确地进行跨媒体检索,需要挖掘、学习不同类型多媒体对象之间的语义关联,为此提出一种基于综合推理模型的多媒体语义挖掘和跨媒体检索技术.首先根据多媒体对象的底层特征构造推理源,根据多媒体对象的共生关系构造影响源场来进行综合推理,并构造出多媒体语义空间;然后针对不同检索例子,根据伪相关反馈为每一个检索例子自适应地选择不同的榆索方法进行跨媒体检索.为了处理检索例子不在训练集合内的情况,提出了两阶段学习方法完成检索;同时还提出了一种基于日志的长程反馈学习算法,以提高系统性能.实验结果证明,该技术能够准确地挖掘多媒体语义,多媒体文档检索和跨媒体检索效果准确_凡稳定.     

多媒体数据挖掘技术研究

本文提出一种多媒体数据挖掘系统的一般结构和挖掘的过程,说明了不同类型多媒体数据挖掘的基本方法和技术,并对多媒体数据提出了阐述和展望。     

多媒体数据挖掘的体系结构和方法

提出了一个多媒体数据挖掘系统的一般结构(M3)，包括多媒体数据库(MD)、多媒体挖掘引擎(MME)和多媒体挖掘界面(MMI)，重点分析了几种挖掘方法(分类、关联和聚类)在多媒体挖掘中的应用。针对不同的媒体，如图像、音频、视频，讨论了各自的挖掘特点和主要挖掘内容。     

用物联网架构建立人性化多媒体管理系统

物联网产业的发展是以应用为先导,主要致力于扩大应用规模和领域,挖掘新的应用潜力。文章阐述了物联网技术在多媒体管理系统中的应用,并从物联网的架构建立更加人性化的多媒体管理系统方面入手,设计了一个基于物联网技术的多媒体管理系统;同时,给出了重点设计的系统架构,分析了其中的技术难点,并提出了相应的解决方案。     

分布式多媒体数据库应用

本文从实践出发，探讨分布式多媒体数据库应用技术，旨在尽可能多地挖掘分布式数据库系统SYBASE在多媒体方面的潜力和优势。     

基于WEB的多媒体数据挖掘的研究

研究多媒体形式的数据的挖掘是目前一个很前沿的课题,它涉及到很多的领域包括网络、多媒体的技术、数据库以及关于知识的决策等。基于通常的一些数据库中所含的数据与多媒体的数据在一些特性上存在很多的不同点,也就使得在数据的挖掘上应用一些相对常规的方法没有办法实现特性上区别挖掘。这就需要研究出能够适用于挖掘多媒体数据的方法以及相应的技术。本研究介绍的是基于WEB技术实现多媒体数据的挖掘。     

职业教育多媒体实验室的设计与建造

本文提出了一般网络化计算机多媒体实验室涉及的载体,以及工科职业教育师资专业对多媒体实验室的功能及使用要求,从而给出的多媒体实验室的设计原则更另适合工科对学生培养目标的要求,将信息技术,计算机多媒体技术,网络技术与工科的职业教育科学更进一步完善地结合在一起。文章最后给出了同济大学职业教育多媒体实验室的建造实例。     

多媒体技术发展刍议

本文通过对多媒体技术发展的简单介绍,提出多媒体技术使用的硬件要求,并对多媒体网络的技术进行了深入的研究,同时介绍了多媒体技术在现实生活中的应用.     

多媒体技术发展刍议

本文通过对多媒体技术发展的简单介绍,提出多媒体技术使用的硬件要求,并对多媒体网络的技术进行了深入的研究,同时介绍了多媒体技术在现实生活中的应用。     

多媒体工作站的发展概况

多媒体技术是９０年代计算机技术发展的重要方面。工作站则是多媒体发展的关键，一旦把多媒体引入工作站，将会使工作站如虎添翼。本文从技术角度介绍了目前国餐多媒体工作站的发展水平。在简要回顾了第一代多媒体工作站的诞生及主要功能和技术之后，文中提出了提高多媒体工作站性能的几项技术，并着重指出了实现多媒体网络对拓宽多媒体工作站应用的重要性。     

多媒体数据的聚簇开采

Internet的普及使多媒体信息的信息量急剧增大,因而,多媒体数据开采逐渐引起人们的关注。文章基于多媒体数据的特点,结合多媒体信息检索技术和数据开采方法,提出了多媒体数据开采系统的基本框架,并给出多媒体数据上的一种聚簇开采算法MDC。     

多媒体开采初探

1 引言对于多媒体的管理,早期采用文件的管理方式。70到80年代期间,数据库学派牵头,采用关键词的描述方法建立媒体数据的索引以达到管理的目的。90年代以后,人们转向研究面向网络环境下的支持基于内容检索的大规模的多媒体数据库。基于内容的检索在一定程度上解决了信息搜索和资源发现的问题,但是基于内容的检索只能获取用户要求的相关信息,而不能从大量多媒体数据中分析出隐含的有价值的模式和知     

图像数据挖掘之研究

从介绍一个图像数据挖掘可以采用的系统原型出发,对图像数据挖掘的常用方法和基本过程进行了详细的阐述。     

A Multimedia Data Mining Framework: Mining Information from Traffic Video Sequences

The analysis and mining of traffic video sequences to discover important but previously unknown knowledge such as vehicle identification, traffic flow, queue detection, incident detection, and the spatio-temporal relations of the vehicles at intersections, provide an economic approach for daily traffic monitoring operations. To meet such demands, a multimedia data mining framework is proposed in this paper. The proposed multimedia data mining framework analyzes the traffic video sequences using background subtraction, image/video segmentation, vehicle tracking, and modeling with the multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over traffic intersections. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-à-vis traffic operations. Three real-life traffic video sequences obtained from different sources and with different weather conditions are used to illustrate the effectiveness and robustness of the proposed multimedia data mining framework by demonstrating how the proposed framework can be applied to traffic applications to answer the spatio-temporal queries.     

科学数据库基于内容的多媒体检索系统

科学数据库中存在大量的多媒体数据,为了实现对多媒体内容的有效存储、管理和检索．基于内容的多媒体综合检索技术将成为技术研究的重点。本文首先分析了科学数据库多媒体资源的特点和对多媒体内容管理的需求．然后探讨了基于内容的多媒体检索技术的原理、特点和检索方法。最后提出了科学数据库多媒体检索系统的一套设计方案,并说明了该体系的结构和功能。     

Multimedia indexing and retrieval: ever great challenges

In this introduction, we present a brief state of the art of multimedia indexing and retrieval as well as highlight some notions explored in the special issue. We hope that the contributions of this special issue will present ingredients for further investigations on this ever challenging domain. The special issue is actually situated between old problems and new challenges, and contribute to understand the next multimedia indexing and retrieval generation. The contributions explore wide range of fields such as signal processing, data mining and information retrieval.     

基于多媒体数据库的关联规则挖掘系统模型

提出了一种多媒体数据库的关联规则挖掘系统模型。介绍了模型的组成，分述了特征提取组件、特征库、挖掘部件3个主要部分，并且介绍了主要的挖掘算法。     

Extracting semantic concepts from images: a decisive feature pattern mining approach

One major challenge in the content-based image retrieval (CBIR) and computer vision research is to bridge the so-called “semantic gap” between low-level visual features and high-level semantic concepts, that is, extracting semantic concepts from a large database of images effectively. In this paper, we tackle the problem by mining the decisive feature patterns (DFPs). Intuitively, a decisive feature pattern is a combination of low-level feature values that are unique and significant for describing a semantic concept. Interesting algorithms are developed to mine the decisive feature patterns and construct a rule base to automatically recognize semantic concepts in images. A systematic performance study on large image databases containing many semantic concepts shows that our method is more effective than some previously proposed methods. Importantly, our method can be generally applied to any domain of semantic concepts and low-level features. Wei Wang received his Ph.D. degree in Computing Science and Engineering from the State University of New York (SUNY) at Buffalo in 2004, under Dr. Aidong Zhang's supervision. He received the B.Eng. in Electrical Engineering from Xi'an Jiaotong University, China in 1995 and the M.Eng. in Computer Engineering from National University of Singapore in 2000, respectively. He joined Motorola Inc. in 2004, where he is currently a senior research engineer in Multimedia Research Lab, Motorola Applications Research Center. His research interests can be summarized as developing novel techniques for multimedia data analysis applications. He is particularly interested in multimedia information retrieval, multimedia mining and association, multimedia database systems, multimedia processing and pattern recognition. He has published 15 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of IADIS International Conference e-Society 2005 and 2006, and has been a reviewer for some leading academic journals and conferences. In 2005, his research prototype of “seamless content consumption” was awarded the “most innovative research concept of the year” from the Motorola Applications Research Center. Dr. Aidong Zhang received her Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at the State University of New York at Buffalo. Her research interests include bioinformatics, data mining, multimedia systems, content-based image retrieval, and database systems. She has authored over 150 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Dr. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACMMultimedia Systems, the International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER Award and SUNY Chancellor's Research Recognition Award.     

Canonical processes for creating personalized semantically rich multimedia presentations

Authoring of multimedia content can be considered as composing media assets such as images, videos, text, and audio in time, space, and interaction into a coherent multimedia presentation. Personalization of such content means that it reflects the users’ or user groups’ profile information and context information. Enriching the multimedia content with semantically rich metadata allows for a better search and retrieval of the content. To actually create personalized semantically-rich multimedia content, a manual authoring of the many different documents for all the different users’ and user groups’ needs is not feasible. Rather a (semi-)automatic authoring of the content seems reasonable. We have analyzed in detail today’s approaches and systems for authoring, personalizing, and semantically enriching multimedia presentations. Based on this analysis, we derived a general creation chain for the (semi-)automatic generation of such content. In this paper, we introduce this creation chain. We present our software engineering support for the chain, the component framework SemanticMM4U. The canonical processes supported by the creation chain and SemanticMM4U framework are described in detail. We also provide an explicit mapping of SemanticMM4U framework components to the processes and argue for the benefits of defining canonical processes for creating personalized semantically rich multimedia presentations.