Video object segmentation aggregation

© 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

Domain Alignment and Temporal Aggregation for Unsupervised Video Object Segmentation

Unsupervised video object segmentation aims at detecting and segmenting the most salient object in videos. In recent times, two-stream approaches that collaboratively leverage appearance cues and motion cues have attracted extensive attention thanks to their powerful performance. However, there are two limitations faced by those methods: 1) the domain gap between appearance and motion information is not well considered; and 2) long-term temporal coherence within a video sequence is not exploited. To overcome these limitations, we propose a domain alignment module (DAM) and a temporal aggregation module (TAM). DAM resolves the domain gap between two modalities by forcing the values to be in the same range using a cross-correlation mechanism. TAM captures long-term coherence by extracting and leveraging global cues of a video. On public benchmark datasets, our proposed approach demonstrates its effectiveness, outperforming all existing methods by a substantial margin