A user attention model for video summarization
[DBLP_Link] [Online_Version] CitedBy 224-
Abstract:
Automatic generation of video summarization is one of the key techniques in video management and browsing. In this paper, we present a generic framework of video summarization based on the modeling of viewer's attention. Without fully semantic understanding of video content, this framework takes advantage of understanding of video content, this framework takes advantage of computational attention models and eliminates the needs of complex heuristic rules in video summarization. A set of methods of audio-visual attention model features are proposed and presented. The experimental evaluations indicate that the computational attention based approach is an effective alternative to video semantic analysis for video summarization.
- Year: 2002
- Pages: 10
- In Proceedings: ACM Multimedia
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Authors:
Yu-Fei Ma
H-index: 11; Papers: 24; Citation: 765 [FOAF] Expertise: Large-scale video retrieval; Scalable video coding / Rate control;
Lie Lu
(Researcher, Speech Group Microsoft Research Asia)
H-index: 18; Papers: 44; Citation: 1623 [FOAF] Homepage: http://research.microsoft.com/users/llu/ Expertise: Large-scale video retrieval;
HongJiang Zhang
(Managing Director, Microsoft Research Asia Advanced Technology Center 5/F, Beijing Sigma Center)
H-index: 60; Papers: 285; Citation: 14203 [FOAF] Homepage: http://research.microsoft.com/~hjzhang/ Expertise: Large-scale video retrieval; Scalable video coding / Rate control; Wavelet image coding / Video compression; Object Recognition / Two-View Motion Estimation; Web Mining;
Mingjing Li
(Researcher , Microsoft Research China)
H-index: 28; Papers: 119; Citation: 2433 [FOAF] Homepage: http://research.microsoft.com/users/mjli/ Expertise: Large-scale video retrieval; Scalable video coding / Rate control; Information Retrieval / Probabilistic Indexing; Character Recognition;
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