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Uncertainty Quantification for Semi-Supervised Multi-class Classification in Ego-Motion Analysis of Body-Worn Videos

Banff International Research Station for Mathematical Innovation and Discovery

Applications such as police body-worn video cameras generate a huge amount of data, beyond what is humanly possible for analysts to review. Such problems are ripe for the development of semi-supervised learning algorithms, which, by definition, use a small amount of training data. We introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class classification problems. We not only predict the class label for each data point but also provide a confidence score for the prediction. We adopt a Bayesian approach and propose a graphical multi-class probit model together with an effective Gibbs sampling procedure. Furthermore, we propose a confidence measure for each data point that correlates with the classification performance. We use the empirical properties of the proposed confidence measure to guide the design of a human-in-the-loop system. The uncertainty quantification algorithm and the human-in-the-loop system are successfully applied to classification problems in image processing and ego-motion analysis of body-worn videos.

Mathematics; Game theory, economics, social and behavioral sciences; Computer science

video/mp4

Author affiliation: UCLA

2019-09-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71694

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/