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Video Content Analysis Using Multimodal Information

Name: Video Content Analysis Using Multimodal Information
Brand: Springer, Berlin
SKU: 9781441953650
Price: 99.88 EUR
Availability: InStock

For Movie Content Extraction, Indexing and Representation

Autor: ing Li, C.C. Jay Kuo

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer, Berlin

Dostupnosť: Skladom u dodávateľa

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With the fast growth ofmultimedia information, content-based video anal ysis, indexing and represent...

Informácie o knihe

Autor

ing Li, C.C. Jay Kuo

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2010

Stránok

194

EAN

9781441953650

ISBN

1441953655

Enbook ID

02706623

Vydavateľ

Springer, Berlin

Hmotnosť

316

Rozmery

155 x 235 x 11

Kompletný popis

With the fast growth ofmultimedia information, content-based video anal ysis, indexing and representation have attracted increasing attention in re cent years. Many applications have emerged in these areas such as video on-demand, distributed multimedia systems, digital video libraries, distance learning/education, entertainment, surveillance and geographical information systems. The need for content-based video indexing and retrieval was also rec ognized by ISOIMPEG, and a new international standard called "Multimedia Content Description Interface" (or in short, MPEG-7)was initialized in 1998 and finalized in September 2001. In this context, a systematic and thorough review ofexisting approaches as well as the state-of-the-art techniques in video content analysis, indexing and representation areas are investigated and studied in this book. In addition, we will specifically elaborate on a system which analyzes, indexes and abstracts movie contents based on the integration ofmultiple media modalities. Content ofeach part ofthis book is briefly previewed below. In the first part, we segment a video sequence into a set ofcascaded shots, where a shot consistsofone or more continuouslyrecorded image frames. Both raw and compressedvideo data will beinvestigated. Moreover, consideringthat there are always non-story units in real TV programs such as commercials, a novel commercial break detection/extraction scheme is developed which ex ploits both audio and visual cues to achieve robust results. Specifically, we first employ visual cues such as the video data statistics, the camera cut fre quency, and the existenceofdelimiting black frames between commercials and programs, to obtain coarse-level detection results.

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Video Content Analysis Using Multimodal Information

For Movie Content Extraction, Indexing and Representation

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