Co-training on multi-view unlabelled data

Authors:
Michał Lewandowski;James Orwell
Affiliations:
Kingston University, London, United Kingdom;Kingston University, London, United Kingdom
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

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Abstract

A novel co-training framework is proposed for object orientation estimation in a multi-camera network environment. The model is initialised using a small labelled dataset and then iteratively boosted using large amount of unlabelled data which are generated automatically from videos. This optimisation process is guided by pairwise constraints of known orientation difference between two views of an object. The introduced methodology is combined with Support Vector Machine and Expectation-Maximization algorithm. The thorough experimental evaluation using 3 datasets of football players, pedestrians and cars confirms the superiority of the boosted models for a robust orientation estimation.