Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers

Authors

Pei Yin (Georgia Tech, Atlanta, GA, USA)
Antonio Crimisini (Microsoft Research, Cambridge, UK)
John Winn (Microsoft Research, Cambridge, UK)
Irfan Essa (Georgia Tech, Atlanta, GA, USA)

Abstract

This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely
approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that
comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence
of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a
novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the
segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce
a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known
classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion
context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then
achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which
requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo
systems.

Paper

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Citation

@article{authorYY,
   author = {First Author, Second Author, etc.},
   title = {My Title},
   journal = {My Journal},
   year = {YYYY},
 }

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Funding

This research is supported by:

NSF Grant
MSR Research Fellowship
Google Grant

Media Coverage / Links to similar projects

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