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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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Conceptual Modeling for New Information Systems Technologies - ER 2001 Workshops, HUMACS, DASWIS, ECOMO, and DAMA, Yokohama Japan, November 27-30, 2001. Revised Papers (Paperback, Revised edition)
Hiroshi Arisawa, Yahiko Kambayashi
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R1,804
Discovery Miles 18 040
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed joint post-proceedings of four international workshops held in conjunction with the 20th International Conference on Conceptual Modeling, ER 2001, held in Yokohama, Japan in November 2001.The 37 revised full papers presented were carefully selected and improved during two rounds of reviewing and revision. In accordance with the respective workshops, the papers are organized in topical sections on conceptual modeling and human, organizational, and social aspects; data semantics in web information systems; conceptual modeling approaches for e-business; and global data modeling.
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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