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
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. ,...
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
Springer US
2000
|
| Ausgabe: | 1st ed. 2000 |
| Schriftenreihe: | IFIP Advances in Information and Communication Technology
40 |
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-0-387-35504-7 https://doi.org/10.1007/978-0-387-35504-7 |
| Zusammenfassung: | 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 |
| Umfang: | 1 Online-Ressource (XIV, 410 p) |
| ISBN: | 9780387355047 |
| DOI: | 10.1007/978-0-387-35504-7 |
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| 520 | |a 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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| spelling | 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 edited by Hiroshi Arisawa, Tiziana Catarci 1st ed. 2000 New York, NY Springer US 2000 1 Online-Ressource (XIV, 410 p) txt rdacontent c rdamedia cr rdacarrier IFIP Advances in Information and Communication Technology 40 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 Data Structures and Information Theory Medicine/Public Health, general Information Storage and Retrieval Information Systems Applications (incl. Internet) Computer Imaging, Vision, Pattern Recognition and Graphics Data structures (Computer science) Medicine Information storage and retrieval Application software Optical data processing Visuelles Datenbanksystem (DE-588)4373475-3 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2000 Fukuoka gnd-content Visuelles Datenbanksystem (DE-588)4373475-3 s DE-604 Arisawa, Hiroshi edt Catarci, Tiziana edt Erscheint auch als Druck-Ausgabe 9781475744576 Erscheint auch als Druck-Ausgabe 9780792378358 Erscheint auch als Druck-Ausgabe 9781475744569 https://doi.org/10.1007/978-0-387-35504-7 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | 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 Data Structures and Information Theory Medicine/Public Health, general Information Storage and Retrieval Information Systems Applications (incl. Internet) Computer Imaging, Vision, Pattern Recognition and Graphics Data structures (Computer science) Medicine Information storage and retrieval Application software Optical data processing Visuelles Datenbanksystem (DE-588)4373475-3 gnd |
| subject_GND | (DE-588)4373475-3 (DE-588)1071861417 |
| 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 |
| title_auth | 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 |
| title_exact_search | 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 |
| title_full | 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 edited by Hiroshi Arisawa, Tiziana Catarci |
| title_fullStr | 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 edited by Hiroshi Arisawa, Tiziana Catarci |
| title_full_unstemmed | 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 edited by Hiroshi Arisawa, Tiziana Catarci |
| title_short | Advances in Visual Information Management |
| title_sort | 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 |
| title_sub | Visual Database Systems. IFIP TC2 WG2.6 Fifth Working Conference on Visual Database Systems May 10–12, 2000, Fukuoka, Japan |
| topic | Data Structures and Information Theory Medicine/Public Health, general Information Storage and Retrieval Information Systems Applications (incl. Internet) Computer Imaging, Vision, Pattern Recognition and Graphics Data structures (Computer science) Medicine Information storage and retrieval Application software Optical data processing Visuelles Datenbanksystem (DE-588)4373475-3 gnd |
| topic_facet | Data Structures and Information Theory Medicine/Public Health, general Information Storage and Retrieval Information Systems Applications (incl. Internet) Computer Imaging, Vision, Pattern Recognition and Graphics Data structures (Computer science) Medicine Information storage and retrieval Application software Optical data processing Visuelles Datenbanksystem Konferenzschrift 2000 Fukuoka |
| url | https://doi.org/10.1007/978-0-387-35504-7 |
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