Ying Li; C.C. Jay Kuo Video Content Analysis Using Multimodal Information 9781441953650

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Автор: Ying Li; C.C. Jay Kuo
Название: Video Content Analysis Using Multimodal Information
ISBN: 9781441953650
Издательство: Springer
Классификация:

Электроника

Графические и цифровые медиа-приложения

Алгоритмы и структуры данных

ISBN-10: 1441953655
Обложка/Формат: Paperback
Страницы: 194
Вес: 0.31 кг.
Дата издания: 07.12.2010
Язык: English
Размер: 234 x 156 x 12
Основная тема: Computer Science
Подзаголовок: For Movie Content Extraction, Indexing and Representation
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.

Video Content Analysis Using Multimodal Information, Ying Li; C.C. Jay Kuo