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We present an approach to discover and segment foreground object(s) in video. Given an unannotated video sequence, the method first identifies object-like regions in any frame according to both static and dynamic cues. We then compute a series of binary partitions among those candidate "key-segments" to discover hypothesis groups with persistent appearance and motion. Finally, using each ranked hypothesis in turn, we estimate a pixel-level object labeling across all frames, where (a) thedoi:10.1109/iccv.2011.6126471 dblp:conf/iccv/LeeKG11 fatcat:722kgevi5jhipdcad2m6nafz5q