E-GrabCut： an economic method of iterative video object extraction
Efficient, interactive foreground/background segmentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut concentrating on achieving both of the segmentation quality and time efficienc...
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| Veröffentlicht in: | Frontiers of Computer Science Jg. 11; H. 4; S. 649 - 660 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Beijing
Higher Education Press
01.08.2017
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 2095-2228, 2095-2236 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Efficient, interactive foreground/background segmentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut concentrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimization process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model (GMM). Thirdly, a robust algorithm for the following frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and segmentation quality. |
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| Bibliographie: | 11-5731/TP interactive video object extraction, video seg-mentation, GrabCut, GMM Efficient, interactive foreground/background seg- mentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut con- centrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimiza- tion process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model （GMM）. Thirdly, a robust algorithm for the follow- ing frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and seg- mentation quality. GrabCut Document received on :2015-12-26 Document accepted on :2016-06-17 video segmentation interactive video object extraction GMM ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2095-2228 2095-2236 |
| DOI: | 10.1007/s11704-016-5558-7 |