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Authors: Julien Bohné 1 ; Sylvain Colin 2 ; Stéphane Gentric 2 and Massimiliano Pontil 3

Affiliations: 1 Safran Morpho and University College London, France ; 2 Safran Morpho, France ; 3 University College London, United Kingdom

ISBN: 978-989-758-173-1

Keyword(s): Similarity Function, Uncertain Data, Missing Data, Face Recognition.

Related Ontology Subjects/Areas/Topics: Applications ; Bayesian Models ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Missing Data ; Multimedia ; Multimedia Signal Processing ; Pattern Recognition ; Similarity and Distance Learning ; Telecommunications ; Theory and Methods

Abstract: Similarity functions are at the core of many pattern recognition applications. Standard approaches use feature vectors extracted from a pair of images to compute their degree of similarity. Often feature vectors are noisy and a direct application of standard similarly learning methods may result in unsatisfactory performance. However, information on statistical properties of the feature extraction process may be available, such as the covariance matrix of the observation noise. In this paper, we present a method which exploits this information to improve the process of learning a similarity function. Our approach is composed of an unsupervised dimensionality reduction stage and the similarity function itself. Uncertainty is taken into account throughout the whole processing pipeline during both training and testing. Our method is based on probabilistic models of the data and we propose EM algorithms to estimate their parameters. In experiments we show that the use of uncertainty signi ficantly outperform other standard similarity function learning methods on challenging tasks. (More)

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Paper citation in several formats:
Bohné, J.; Colin, S.; Gentric, S. and Pontil, M. (2016). Similarity Function Learning with Data Uncertainty.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 131-140. DOI: 10.5220/0005648601310140

@conference{icpram16,
author={Julien Bohné. and Sylvain Colin. and Stéphane Gentric. and Massimiliano Pontil.},
title={Similarity Function Learning with Data Uncertainty},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={131-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005648601310140},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Similarity Function Learning with Data Uncertainty
SN - 978-989-758-173-1
AU - Bohné, J.
AU - Colin, S.
AU - Gentric, S.
AU - Pontil, M.
PY - 2016
SP - 131
EP - 140
DO - 10.5220/0005648601310140

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