Robust video summarization using collaborative representation of adjacent frames

Robust video summarization using collaborative representation of adjacent frames Multimed Tools Appl https://doi.org/10.1007/s11042-018-6053-y Robust video summarization using collaborative representation of adjacent frames 1 1 1 Mingyang Ma · Shaohui Mei · Shuai Wan · 2 2 Zhiyong Wang · David Dagan Feng Received: 8 January 2018 / Revised: 2 April 2018 / Accepted: 23 April 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage a large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. However, these existing approaches treat each frame indepen- dently, so the performance can be greatly influenced by each individual frame. In this paper, we formulate the VS problem with a collaborative representation model to take the visual similarity of adjacent frames into consideration. To be specific, during the procedure of reconstruction, both each individual frame and their adjacent frames are reconstructed col- laboratively, so the impact of an individual frame can be weakened. In addition, a greedy iterative algorithm is designed for model optimization, where the sparsity and the aver- age percentage of reconstruction (APOR) are adopted to control the iteration. Experimental http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Robust video summarization using collaborative representation of adjacent frames

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6053-y
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6053-y Robust video summarization using collaborative representation of adjacent frames 1 1 1 Mingyang Ma · Shaohui Mei · Shuai Wan · 2 2 Zhiyong Wang · David Dagan Feng Received: 8 January 2018 / Revised: 2 April 2018 / Accepted: 23 April 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage a large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. However, these existing approaches treat each frame indepen- dently, so the performance can be greatly influenced by each individual frame. In this paper, we formulate the VS problem with a collaborative representation model to take the visual similarity of adjacent frames into consideration. To be specific, during the procedure of reconstruction, both each individual frame and their adjacent frames are reconstructed col- laboratively, so the impact of an individual frame can be weakened. In addition, a greedy iterative algorithm is designed for model optimization, where the sparsity and the aver- age percentage of reconstruction (APOR) are adopted to control the iteration. Experimental

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 1, 2018

References

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