Video Saliency Detection Using Object Proposals
Guo, Fang1; Wang, Wenguan1; Shen, Jianbing1; Shao, Ling2; Yang, Jian3; Tao, Dacheng4,5; Tang, Yuan Yan6
AbstractIn 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.
KeywordObject proposals object-level saliency cues salient region detection video saliency
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Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000447825400012
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Cited Times [WOS]:22   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Beijing Inst Technol, Sch Comp Sci, Beijing Key Lab Intelligent Informat Technol, Beijing 100081, Peoples R China;
2.Univ East Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England;
3.Beijing Inst Technol, Sch Optoelect, Beijing Engn Res Ctr Mixed Real & Adv Display, Beijing 100081, Peoples R China;
4.Univ Sydney, Fac Engn & Informat Technol, UBTECH Sydney Artificial Intelligence Ctr, Darlington, NSW 2008, Australia;
5.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Darlington, NSW 2008, Australia;
6.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
Recommended Citation
GB/T 7714
Guo, Fang,Wang, Wenguan,Shen, Jianbing,et al. Video Saliency Detection Using Object Proposals[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(11):3159-3170.
APA Guo, Fang.,Wang, Wenguan.,Shen, Jianbing.,Shao, Ling.,Yang, Jian.,...&Tang, Yuan Yan.(2018).Video Saliency Detection Using Object Proposals.IEEE TRANSACTIONS ON CYBERNETICS,48(11),3159-3170.
MLA Guo, Fang,et al."Video Saliency Detection Using Object Proposals".IEEE TRANSACTIONS ON CYBERNETICS 48.11(2018):3159-3170.
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