Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation

Ying Li; Kuo, C.C. Jay

ISBN 10: 1402074905 / ISBN 13: 9781402074905

Published by Springer, 2003

About this Item

In.

Seller Inventory # ria9781402074905_new

Bibliographic Details

• Title: Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 1402074905
• ISBN 13: 9781402074905
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.