Multimedia networking applications and, in particular, the transport of c- pressed video are expected to contribute signi?cantly to the tra?c in the future Internet and wireless networks. For transport over networks, video is typically encoded (i. e., compressed) to reduce the bandwidth requirements. Even compressed video, however, requires large bandwidths of the order of hundred kbps or Mbps. In addition, compressed video streams typically - hibit highly variable bit rates (VBR) as well as long range dependence (LRD) properties. This, in conjunction with the stringent Quality of Service (QoS) requirements (loss and delay) of video tra?c, makes the transport of video tra?covercommunicationnetworksachallengingproblem. Asaconsequence, in the last decade the networking research community has witnessed an - plosion in research on all aspects of video transport. The characteristics of video tra?c, video tra?c modeling, as well as protocols and mechanisms for the e?cient transport of video streams, have received a great deal of interest among networking researchers and network operators and a plethora of video transport schemes have been developed. For developing and evaluating video transport mechanisms and for - search on video networking in general, it is necessary to have available some characterizationofthevideo. Generally, therearethreedi?erentwaystoch- acterize encoded video for the purpose of networking research: (i)video tra?c model, (ii) video bit stream, and (iii) video tra?c trace
The area of content-based video retrieval is a very hot area both for research and for commercial applications. In order to design effective video databases for applications such as digital libraries, video production, and a variety of Internet applications, there is a great need to develop effective techniques for content-based video retrieval. One of the main issues in this area of research is how to bridge the semantic gap between low-Ievel features extracted from a video (such as color, texture, shape, motion, and others) and semantics that describe video concept on a higher level. In this book, Dr. Milan Petkovi6 and Prof. Dr. Willem Jonker have addressed this issue by developing and describing several innovative techniques to bridge the semantic gap. The main contribution of their research, which is the core of the book, is the development of three techniques for bridging the semantic gap: (1) a technique that uses the spatio-temporal extension of the Cobra framework, (2) a technique based on hidden Markov models, and (3) a technique based on Bayesian belief networks. To evaluate performance of these techniques, the authors have conducted a number of experiments using real video data. The book also discusses domains solutions versus general solution of the problem. Petkovi6 and Jonker proposed a solution that allows a system to be applied in multiple domains with minimal adjustments. They also designed and described a prototype video database management system, which is based on techniques they proposed in the book.
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