Издательство Springer, 2008, -282 pp. The evolution of technology has set the stage for the rapid growth of the video Web: broadband Internet access is ubiquitous, and streaming media protocols, systems, and encoding standards are mature. In addition to Web video delivery, users can easily contribute content captured on low cost camera phones and other consumer products. The media and entertainment industry no longer views these developments as a threat to their established business practices, but as an opportunity to provide services for more viewers in a wider range of consumption contexts. The emergence of IPTV and mobile video services offers unprecedented access to an ever growing number of broadcast channels and provides the flexibility to deliver new, more personalized video services. Highly capable portable media players allow us to take this personalized content with us, and to consume it even in places where the network does not reach. Video search engines enable users to take advantage of these emerging video resources for a wide variety of applications including entertainment, education and communications. However, the task of information extraction from video for retrieval applications is challenging, providing opportunities for innovation. This book aims to first describe the current state of video search engine technology and second to inform those with the requisite technical skills of the opportunities to contribute to the development of this field. Today’s Web search engines have greatly improved the accessibility and therefore the value of the Web. The top portals prominently feature search capabilities and go beyond text search to include image and video search in various forms. A number of smaller companies have begun to offer more sophisticated media search features based on content analysis. Academic research groups have been actively developing algorithms and prototypes in this area for over a decade; incorporating and advancing previously existing constituent technologies. Most media search systems rely on available metadata or contextual information in text form. Syndication formats such as RSS provide organized access to media sources and include descriptive global metadata. While these information sources are valuable and should be exploited, they are limited because they are typically brief, high level and subjective. . Therefore the current focus of media indexing research is to develop algorithms to exploit the media content itself as much as possible to augment available metadata. In some cases, the media may contain associated text streams such as closed caption or song lyrics. By extracting and operating on these streams, a textual representation of the dialog is obtained and existing text information retrieval methods can then be applied to retrieve relevant media. Speech recognition can be employed to create an approximation of the transcription, and techniques such as video optical character recognition can also be used to generate a textual representation of the media content. Although these technologies are inherently error prone, they have been used with success for indexing applications. Advanced speech retrieval systems use phonetic search to deal with the out of vocabulary problem and maintain alternative hypotheses in the form of lattices to boost recall. Media retrieval that goes beyond the textual media component is more complex because the basic media features are not well defined and may not scale well for large archives. Further, formulating queries may not be as simple as typing a keyword. However, systems have been designed to, for example, retrieve images similar to a given image (query by example) or retrieve images based on a specification of color or shape. For navigating video retrieval results, techniques such as video skimming or mosaicing have been proposed. The book will have a practical emphasis with the goal of bringing researchers up to date on the state of the art in multimedia search technologies and systems. Part of the presentation will follow a logical flow from content acquisition, analysis to extract index data, data representation, media archival, retrieval and finally rendering results in a Web-based environment. Each of these major functional components will be outlined, and particular emphasis will be given to automated content analysis techniques since this is critical for operating video search engines at scale, and it presents on-going research challenges. To give the readers an understanding of the issues involved, individual media processing algorithms operating on text, audio and video will be addressed including text alignment, case restoration, entity extraction, speech recognition, speaker segmentation, and video shot boundary detection. Additionally, the value of operating on multiple media components simultaneously will be illustrated by examining multimodal processing techniques, e.g. for media segmentation. The role of media segmentation in improving relevance ranking for long-form content will be discussed.Video Search. Video Data Sources and Applications. Internet Video. Video Search Engine Systems. Media Processing. Video Processing. Audio Processing. Text Processing. Multimodal Processing. Research Systems. Current Trends in Video Search.
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