Utility-based summarization of home videos

Truong, Ba Tu and Venkatesh, Svetha 2007, Utility-based summarization of home videos, in MMM'07 : Advances in multimedia modeling : Proceedings of the 13th International Multimedia Modeling Conference, Springer-Verlag Berlin Heidelberg, Berlin, Germany, pp. 505-516, doi: 10.1007/978-3-540-69423-6_49.

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Title Utility-based summarization of home videos
Author(s) Truong, Ba Tu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Multimedia Modeling Conference (13th : 2007 : Singapore, Singapore)
Conference location Singapore, Singapore
Conference dates 9-12 Jan. 2007
Title of proceedings MMM'07 : Advances in multimedia modeling : Proceedings of the 13th International Multimedia Modeling Conference
Editor(s) Cham, Tat-Jen
Cai, Jianfei
Dorai, Chitra
Rajan, Deepu
Chua, Tat-Seng
Publication date 2007
Conference series International Multimedia Modeling Conference
Start page 505
End page 516
Total pages 12
Publisher Springer-Verlag Berlin Heidelberg
Place of publication Berlin, Germany
Keyword(s) home video
static summarization
identity count
identity recognition
sense of space
activity recognition
Summary The aim of this work is to devise an effective method for static summarization of home video sequences. Based on the premise that the user watching a summary is interested in people related (how many, who, emotional state) or activity related aspects, we formulate a novel approach to video summarization that works to specifically expose relevant video frames that make the content spotting tasks possible. Unlike existing approaches, which work on low-level features which often produce the summary not appealing to the viewer due to the semantic gap between low-level features and high-level concepts, our approach is driven by various utility functions (identity count, identity recognition, emotion recognition, activity recognition, sense of space) that use the results of face detection, face clustering, shot clustering and within cluster frame alignment. The summarization problem is then treated as the problem of extracting the set of key frames that have the maximum combined utility.
ISBN 9783540694212
Language eng
DOI 10.1007/978-3-540-69423-6_49
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044835

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