Video saliency detection using object proposals

Guo, F; Wang, W; Shen, J; Shao, L; Yang, J; Tao, D; Tang, YY

Video saliency detection using object proposals

Guo, F Wang, W Shen, J Shao, L Yang, J Tao, D

Tang, YY

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2018, 48 (11), pp. 3159 - 3170
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Guo, F	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Shao, L	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Tang, YY	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2018, 48 (11), pp. 3159 - 3170	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123866
dc.description.abstract	© 2013 IEEE. In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2017.2761361	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Video saliency detection using object proposals	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	48	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2013 IEEE. In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms.

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http://hdl.handle.net/10453/123866