Video Saliency Detection Using Object Proposals

Fang Guo, Wenguan Wang, Jianbing Shen, Ling Shao, Jian Yang, Dacheng Tao, YY Tang

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)
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Abstract

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.
Original languageEnglish
Pages (from-to)3159-3170
JournalIEEE Transactions on Cybernetics
Volume48
Issue number11
Early online date25 Oct 2017
DOIs
Publication statusPublished - Nov 2018

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