  We investigate the problem of strictly unsupervised video object
segmentation, i.e., the separation of a primary object from background in video
without a user-provided object mask or any training on an annotated dataset. We
find foreground objects in low-level vision data using a John Tukey-inspired
measure of "outlierness". This Tukey-inspired measure also estimates the
reliability of each data source as video characteristics change (e.g., a camera
starts moving). The proposed method achieves state-of-the-art results for
strictly unsupervised video object segmentation on the challenging DAVIS
dataset. Finally, we use a variant of the Tukey-inspired measure to combine the
output of multiple segmentation methods, including those using supervision
during training, runtime, or both. This collectively more robust method of
segmentation improves the Jaccard measure of its constituent methods by as much
as 28%.
