A video synchronization approach for coherent key-frame extraction and object segmentation

Vigneswaran, S; Leelemani, A; Divya, K

A video synchronization approach for coherent key-frame extraction and object segmentation

Vigneswaran, S Leelemani, A Divya, K

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Publisher:: Asian Research Publication Network
Publication Type:: Journal Article
Citation:: Journal of Theoretical and Applied Information Technology, 2014, 68 (2), pp. 331 - 338
Issue Date:: 2014-10-20

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Field	Value	Language
dc.contributor.author	Vigneswaran, S	en_US
dc.contributor.author	Leelemani, A	en_US
dc.contributor.author	Divya, K	en_US
dc.date.issued	2014-10-20	en_US
dc.identifier.citation	Journal of Theoretical and Applied Information Technology, 2014, 68 (2), pp. 331 - 338	en_US
dc.identifier.issn	1992-8645	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117853
dc.description.abstract	© 2005 - 2014 JATIT & LLS. All rights reserved. In this paper we discuss a new video frame synchronization approach for coherent key-frame extraction and object segmentation. As two basic units for content-based video analysis, key-frame extraction and object segmentation are usually implemented independently and separately based on different feature sets. Our previous work showed that by exploiting the inherent relationship between key-frames and objects, a set of salient key-frames can be extracted to support robust and efficient object segmentation. This work furthers the previous numerical studies by suggesting a new analytical approach to jointly formulate key-frame extraction and object segmentation via a statistical mixture model where the concept of frame/pixel saliency which is introduced and also this deals with the relationship between the frames. A modified Expectation Maximization algorithm is developed for model estimation that leads to the most salient key-frames for object segmentation. Simulations on both synthetic and real videos show the effectiveness and efficiency of the proposed method.	en_US
dc.publisher	Asian Research Publication Network	en_US
dc.relation.ispartof	Journal of Theoretical and Applied Information Technology	en_US
dc.title	A video synchronization approach for coherent key-frame extraction and object segmentation	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	68	en_US
utslib.for	090404 Membrane and Separation Technologies	en_US
utslib.for	0907 Environmental Engineering	en_US
utslib.for	090407 Process Control and Simulation	en_US
utslib.for	08 Information And Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access
pubs.declined	2017-09-28T14:12:01.292+1000
pubs.consider-herdc	false	en_US
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	68	en_US

Abstract:

© 2005 - 2014 JATIT & LLS. All rights reserved. In this paper we discuss a new video frame synchronization approach for coherent key-frame extraction and object segmentation. As two basic units for content-based video analysis, key-frame extraction and object segmentation are usually implemented independently and separately based on different feature sets. Our previous work showed that by exploiting the inherent relationship between key-frames and objects, a set of salient key-frames can be extracted to support robust and efficient object segmentation. This work furthers the previous numerical studies by suggesting a new analytical approach to jointly formulate key-frame extraction and object segmentation via a statistical mixture model where the concept of frame/pixel saliency which is introduced and also this deals with the relationship between the frames. A modified Expectation Maximization algorithm is developed for model estimation that leads to the most salient key-frames for object segmentation. Simulations on both synthetic and real videos show the effectiveness and efficiency of the proposed method.

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http://hdl.handle.net/10453/117853