Video Mining / Edition 1

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Video Mining is an essential reference for the practitioners and academicians in the fields of multimedia search engines. Half a terabyte or 9,000 hours of motion pictures are produced around the world every year. Furthermore, 3,000 television stations broadcasting for twenty-four hours a day produce eight million hours per year, amounting to 24,000 terabytes of data. Although some of the data is labeled at the time of production, an enormous portion remains unindexed. For practical access to such huge amounts of data, there is a great need to develop efficient tools for browsing and retrieving content of interest, so that producers and end users can quickly locate specific video sequences in this ocean of audio-visual data. Video Mining is important because it describes the main techniques being developed by the major players in industry and academic research to address this problem. It is the first time research from these leaders in the field developing the next-generation multimedia search engines is being described in great detail and gathered into a single volume. Video Mining will give valuable insights to all researchers and non-specialists who want to understand the principles applied by the multimedia search engines that are about to be deployed on the Internet, in studios' multimedia asset management systems, and in video-on-demand systems.

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Product Details

  • ISBN-13: 9781402075490
  • Publisher: Springer US
  • Publication date: 8/31/2003
  • Series: The International Series in Video Computing, #6
  • Edition description: 2003
  • Edition number: 1
  • Pages: 340
  • Product dimensions: 6.20 (w) x 9.48 (h) x 0.82 (d)

Table of Contents

Preface ix
1 Efficient Video Browsing 1
2 Beyond Key-Frames: The Physical Setting as a Video Mining Primitive 31
3 Temporal Video Boundaries 61
4 Video Summarization using MPEG-7 Motion Activity and Audio Descriptors 91
5 Movie Content Analysis, Indexing and Skimming Via Multimodal Information 123
6 Video OCR: A Survey and Practitioner's Guide 155
7 Video Categorization Using Semantics and Semiotics 185
8 Understanding the Semantics of Media 219
9 Statistical Techniques for Video Analysis and Searching 253
10 Mining Statistical Video Structures 279
11 Pseudo-Relevance Feedback for Multimedia Retrieval 309
Index 339
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