- Shopping Bag ( 0 items )
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.
1. Efficient Video Browsing; A. Amir, S. Srinivasan, D. Ponceleon. 2. Beyond Key-Frames: The Physical Setting as a Video Mining Primitive; A. Aner-Wolf, J.R. Kender. 3. Temporal Video Boundaries; N. Dimitrova, L. Agnihotri, R. Jasinschi. 4. Video Summarization using MPEG-7 Motion Activity and Audio Descriptors; A. Divakaran, K.A. Peker, R. Radhakrishnan, Ziyou Xiong, R. Cabasson. 5. Movie Content Analysis, Indexing and Skimming Via Multimodal Information; Ying Li, S. Narayanan, C.-C. Jay Kuo. 6. Video OCR: A Survey and Practitioner's Guide; R. Lienhart. 7. Video Categorization Using Semantics and Semiotics; Z. Rasheed, M. Shah. 8. Understanding the Semantics of Media; M. Slaney, D. Ponceleon, J. Kaufman. 9. Statistical Techniques for Video Analysis and Searching; J.R. Smith, Ching-Yung Lin, M. Naphade, A. (Paul) Natsev, B. Tseng. 10. Mining Statistical Video Structures; Lexing Xie, Shih-Fu Chang, A. Divakaran, Huifang Sun. 11. Pseudo-Relevance Feedback for Multimedia Retrieval; Rong Yan, A.G. Hauptmann, Rong Jin. Index.