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Volume: 31 | Article ID: art00015
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Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos
  DOI :  10.2352/ISSN.2470-1173.2019.11.IPAS-264  Published OnlineJanuary 2019
Abstract

Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, 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 humanin-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.

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Yiling Qiao, Chang Shi, Chenjian Wang, Hao Li, Matt Haberland, Xiyang Luo, Andrew M Stuart, Andrea L Bertozzi, "Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videosin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVII,  2019,  pp 264-1 - 264-7,  https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-264

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