  Despite recent advances in video segmentation, many opportunities remain to
improve it using a variety of low and mid-level visual cues. We propose
improvements to the leading streaming graph-based hierarchical video
segmentation (streamGBH) method based on early and mid level visual processing.
The extensive experimental analysis of our approach validates the improvement
of hierarchical supervoxel representation by incorporating motion and color
with effective filtering. We also pose and illuminate some open questions
towards intermediate level video analysis as further extension to streamGBH. We
exploit the supervoxels as an initialization towards estimation of dominant
affine motion regions, followed by merging of such motion regions in order to
hierarchically segment a video in a novel motion-segmentation framework which
aims at subsequent applications such as foreground recognition.
