  In this paper, we propose an unsupervised video object co-segmentation
framework based on the primary object proposals to extract the common
foreground object(s) from a given video set. In addition to the objectness
attributes and motion coherence our framework exploits the temporal consistency
of the object-like regions between adjacent frames to enrich the set of
original object proposals. We call the enriched proposal sets temporal proposal
streams, as they are composed of the most similar proposals from each frame
augmented with predicted proposals using temporally consistent superpixel
information. The temporal proposal streams represent all the possible region
tubes of the objects. Therefore, we formulate a graphical model to select a
proposal stream for each object in which the pairwise potentials consist of the
appearance dissimilarity between different streams in the same video and also
the similarity between the streams in different videos. This model is suitable
for single (multiple) foreground objects in two (more) videos, which can be
solved by any existing energy minimization method. We evaluate our proposed
framework by comparing it to other video co-segmentation algorithms. Our method
achieves improved performance on state-of-the-art benchmark datasets.
