Advances in Visual Information Management

Advances in Visual Information Management : Visual Database Systems. IFIP TC2 WG2.6 Fifth Working Conference on Visual Database Systems May 10-12, 2000, Fukuoka, Japan

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Description

Video segmentation is the most fundamental process for appropriate index- ing and retrieval of video intervals. In general, video streams are composed 1 of shots delimited by physical shot boundaries. Substantial work has been done on how to detect such shot boundaries automatically (Arman et aI. , 1993) (Zhang et aI. , 1993) (Zhang et aI. , 1995) (Kobla et aI. , 1997). Through the inte- gration of technologies such as image processing, speech/character recognition and natural language understanding, keywords can be extracted and associated with these shots for indexing (Wactlar et aI. , 1996). A single shot, however, rarely carries enough amount of information to be meaningful by itself. Usu- ally, it is a semantically meaningful interval that most users are interested in re- trieving. Generally, such meaningful intervals span several consecutive shots. There hardly exists any efficient and reliable technique, either automatic or manual, to identify all semantically meaningful intervals within a video stream. Works by (Smith and Davenport, 1992) (Oomoto and Tanaka, 1993) (Weiss et aI. , 1995) (Hjelsvold et aI. , 1996) suggest manually defining all such inter- vals in the database in advance. However, even an hour long video may have an indefinite number of meaningful intervals. Moreover, video data is multi- interpretative. Therefore, given a query, what is a meaningful interval to an annotator may not be meaningful to the user who issues the query. In practice, manual indexing of meaningful intervals is labour intensive and inadequate.
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Product details

  • Hardback | 410 pages
  • 156 x 234 x 23.88mm | 1,710g
  • Dordrecht, Netherlands
  • English
  • 2000 ed.
  • XIV, 410 p.
  • 0792378350
  • 9780792378358

Table of contents

Message from the General Chair. Message from the Program Co-Chairs. Committees. Part I: Advances in Visual Information Management I. 1. Construction of the Multimedia Mediation Systems; M. Sakauchi. Part II: Video Retrieval. 2. A New Algebraic Approach to Retrieve Meaningful Video Intervals from Fragmentarily Indexed Video Shots; S. Pradhan, et al. 3. Toward the MEdiaSys VIdeo Search Engine (MEVISE); F. Andres, et al. 4. Content-based Video Retrieval Based on Similarity of Camera Motion; H. Endoh, R. Kataoka. Part III: Information Visualization. 5. Visual Exploration for Social Recommendations; J. Tatemura. 6. Web-Based Visualization of Large Hierarchical Graphs Using Invisible Links in a Hyperbolic Space; M.C. Hao, et al. 7. Visualizing Electronic Document Repositories: Drawing Books and Papers in a Digital Library; A. Rauber, H. Bina. Part IV: Modeling and Recognition. 8. A Motion Recognition Method by Using Primitive Motions; R. Osaki, et al. Conceptual Modelling for Database User Interfaces; R. Cooper, et al. Part V: Advances in Visual Information Management II. 10. Searching, Data Mining and Visualization of Multimedia Data; C. Faloutsos. Part VI: Image Similarity Retrieval. 11. Efficient Image Retrieval by Examples; R. Brunelli, O. Mich. 12. Applying Augmented Orientation Spatial Similarity Retrieval in Pictorial Database; X.M. Zhou, et al. 13. Toward feature Algebras in Visual Databases: The Case for a Histogram Algebra; A. Gupta, S. Santini.Part VII: Spatio-Temporal Database. 14. Query-By-Trace: Visual Predicate Specification in Spatio-Temporal Databases; M. Erwig, M. Schneider. 15. Skimming Multiple Perspective Video Using Tempo-Spatial Importance Measures; T. Hata, et al. 16. Networked Augmented Spatial Hypermedia System on Internet; M. Murao, et al. Part VIII: Visual Querying. 17. Drag and Drop: Amalgamation of Authoring, Querying, and Restructuring for Multimedia View Construction; A. Morishima, et al. 18. BBQ: A Visual Interface for Integrated Browsing and Querying of XML; K.D. Munroe, Y. Papakonstantiou. 19. MDDQL: A Visual Query Language for Metadata Driven Querying; E. Kapetanios, et al. Part IX: Clustering and Retrieval. 20. Hierarchical Space Model for Multimedia Data Retrieval; M. Onizuka, S. Nishioka. 21. MST Construction with Metric Matrix for Clustering; M. Ishikawa, et al. Part X: User Interface. 22. Automatic Updates of Interactive Information Visualization User Interfaces through Database Triggers; M. Leissler, et al. 23. TBE: A Graphical Interface for Writing Trigger Rules in Active Databases; D. Lee, et al. 24. WEBSA: Database Support for Efficient Web Site Navigation; I.F. Cruz, et al. Index of contributors. Keyword index.
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