Probabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation
Collomosse, JP and wang, T (2012) Probabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation IEEE Transactions on Multimedia, 14 (2). 389 - 400. ISSN 1520-9210
Available under License : See the attached licence file.
We present a robust algorithm for temporally coherent video segmentation. Our approach is driven by multi-label graph cut applied to successive frames, fusing information from the current frame with an appearance model and labeling priors propagated forwarded from past frames. We propagate using a novel motion diffusion model, producing a per-pixel motion distribution that mitigates against cumulative estimation errors inherent in systems adopting “hard” decisions on pixel motion at each frame. Further, we encourage spatial coherence by imposing label consistency constraints within image regions (super-pixels) obtained via a bank of unsupervised frame segmentations, such as mean-shift. We demonstrate quantitative improvements in accuracy over state-of-the-art methods on a variety of sequences exhibiting clutter and agile motion, adopting the Berkeley methodology for our comparative evaluation.
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|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User:||Symplectic Elements|
|Date Deposited:||23 Oct 2012 08:53|
|Last Modified:||23 Sep 2013 19:38|
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