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

Hiroshi Arisawa (u. a.)

ISBN 10: 1475744579 / ISBN 13: 9781475744576

Published by Springer, 2013

About this Item

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 | Hiroshi Arisawa (u. a.) | Taschenbuch | xiv | Englisch | 2013 | Springer | EAN 9781475744576 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 105321929

Bibliographic Details

• Title: 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
• Publisher: Springer
• Publication Date: 2013
• Language: English
• ISBN 10: 1475744579
• ISBN 13: 9781475744576
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 24D millimeters 235 millimeters width by 155 millimeters height by 24 millimeters depth
• Weight: 645 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.