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- Title
Effective online refinement for video object segmentation.
- Authors
Li, Gongyang; Liu, Zhi; Zhou, Xiaofei
- Abstract
In this paper, we propose a novel framework, which deeply explores the motion cue and the online fine-tuning strategy to tackle the task of semi-supervised video object segmentation. First, in order to filter out the irrelevant background regions in the initial segmentation results, which are generated by an existing semi-supervised segmentation model, a motion based background suppression method is exploited to obtain the purified segmentation results. Second, a set of key frames with high-quality segmentation results are selected based on several metrics of segmentation quality in the purified segmentation results. Finally, the selected key frames are combined with the manually annotated first frame to efficiently retrain the segmentation model online, so as to obtain more accurate segmentation results. Our experimental results on two challenging datasets demonstrate that the proposed framework achieves the state-of-the-art performance.
- Subjects
VIDEOS; STREAMING video &; television; MOTION
- Publication
Multimedia Tools & Applications, 2019, Vol 78, Issue 23, p33617
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-019-08146-3