Probabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation
wang, T and Collomosse, JP (2012) Probabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation IEEE Transactions on Multimedia, 14 (2). pp. 389-400.
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.
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Date :||1 April 2012|
|Identification Number :||https://doi.org/10.1109/TMM.2011.2177078|
|Additional Information :||
Copyright 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
|Depositing User :||Symplectic Elements|
|Date Deposited :||23 Oct 2012 08:53|
|Last Modified :||26 Nov 2014 14:33|
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