Similarity Function Learning with Data Uncertainty

Julien Bohné, Sylvain Colin, Stéphane Gentric, Massimiliano Pontil

2016

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 significantly outperform other standard similarity function learning methods on challenging tasks.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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