Budvytis, I and Badrinarayanan, V and Cipolla, R (2011) Semi-supervised video segmentation using tree structured graphical models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 2257-2264. ISSN 1063-6919Full text not available from this repository.
We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which delivers pixel labels alongwith their uncertainty estimates. Our motivation to employ supervision is to tackle a task-specific segmentation problem where the semantic objects are pre-defined by the user. The video model we propose for this problem is based on a tree structured approximation of a patch based undirected mixture model, which includes a novel time-series and a soft label Random Forest classifier participating in a feedback mechanism. We demonstrate the efficacy of our model in cutting out foreground objects and multi-class segmentation problems in lengthy and complex road scene sequences. Our results have wide applicability, including harvesting labelled video data for training discriminative models, shape/pose/articulation learning and large scale statistical analysis to develop priors for video segmentation. © 2011 IEEE.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:14|
|Last Modified:||26 Apr 2017 01:44|