Figure/ground video segmentation using greedy transductive cosegmentation

Cosegmentation has achieved great success in exploiting inter-image segmentation consistency to segment a group of images simultaneously. To enforces non-local temporal coherence across all the frames by high-order object-level appearance/semantic correspondence with a compensation to the short-time window motion coherence cue, this paper cosegments the video frames together with a novel interframe segmentation consistency term. A direct application of existing cosegmentation algorithms to video frames encounters the following challenges: the high correlation of adjacent frames which makes the segmentation ambiguous and a large number of video frames which makes the computation expensive. To tackle them, we formulate the cosegmentation in a transductive learning framework to iteratively learn the inter-frame consistency term from all the video frames. The proposed algorithm is evaluated on the standard SegTrack dataset and promising results are obtained.