Video object segmentation aggregation

Publication Type:
Conference Proceeding
Proceedings - IEEE International Conference on Multimedia and Expo, 2016, 2016-August
Issue Date:
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© 2016 IEEE. We present an approach for unsupervised object segmentation in unconstrained videos. Driven by the latest progress in this field, we argue that segmentation performance can be largely improved by aggregating the results generated by state-of-the-art algorithms. Initially, objects in individual frames are estimated through a per-frame aggregation procedure using majority voting. While this can predict relatively accurate object location, the initial estimation fails to cover the parts that are wrongly labeled by more than half of the algorithms. To address this, we build a holistic appearance model using non-local appearance cues by linear regression. Then, we integrate the appearance priors and spatio-temporal information into an energy minimization framework to refine the initial estimation. We evaluate our method on challenging benchmark videos and demonstrate that it outperforms state-of-the-art algorithms.
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