Paper Info
Title
Video summarization by redundancy removing and content ranking
Abstract
In order to help the user to grasp the long video content quickly, this paper proposes a novel video summarization approach based on redundancy removal and content ranking. By video parsing and cast indexing, the approach first constructs a story board to let user know about the main scenes and the main actors in the video. Then it generates a "story-constraint summary" by key frame clustering and repetitive segment detection. To shorten the video summary length to a target length, our approach constructs a "time-constraint summary" by important factor based content ranking. Extensive experiments are carried out on TV series, movies, and cartoons. Good results demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2007
10.1145/1291233.1291375
ACM Multimedia 2001
Keywords
Field
DocType
time-constraint summary,story-constraint summary,video summary length,long video content,video parsing,content ranking,main actor,main scene,target length,novel video summarization approach,indexation
Automatic summarization,GRASP,Information retrieval,Ranking,Computer science,Search engine indexing,Redundancy (engineering),Video tracking,Key frame,Cluster analysis,Multimedia
Conference
Citations 
PageRank 
References 
4
0.46
16
Authors
7
Name
Order
Citations
PageRank
Tao Wang123823.70
Yue Gao23259124.70
Patricia P. Wang3253.79
Eric Li440.46
Wei Hu518214.17
Yimin Zhang635928.66
Jun-hai Yong762061.47