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{ "item_title" : "Video Content Analysis Using Multimodal Information", "item_author" : [" Ying Li", "C. C. Jay Kuo "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/40/207/490/1402074905_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Video Content Analysis Using Multimodal Information|Ying Li

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

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Overview

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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Details

  • ISBN-13: 9781402074905
  • ISBN-10: 1402074905
  • Publisher: Springer
  • Publish Date: June 2003
  • Dimensions: 9 x 7.02 x 0.72 inches
  • Shipping Weight: 1.11 pounds
  • Page Count: 194

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