ONLINE LEARNING OF GAUSSIAN MIXTURE MODELS - A Two-Level Approach

Arnaud Declercq; Justus H. Piater

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Paper

ONLINE LEARNING OF GAUSSIAN MIXTURE MODELS - A Two-Level Approach

Topics: Real-Time Vision; Visual Learning

In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: OPRMLT, 605-611, 2008 , Funchal, Madeira, Portugal

Authors: Arnaud Declercq and Justus H. Piater

Affiliation: Montefiore Institute, University of Liège, Belgium

Keyword(s): Online learning, Gaussian mixture model, Uncertain model.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Real-Time Vision

Abstract: We present a method for incrementally learning mixture models that avoids the necessity to keep all data points around. It contains a single user-settable parameter that controls via a novel statistical criterion the trade-off between the number of mixture components and the accuracy of representing the data. A key idea is that each component of the (non-overfitting) mixture is in turn represented by an underlying mixture that represents the data very precisely (without regards to overfitting); this allows the model to be refined without sacrificing accuracy.

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Paper citation in several formats:

Declercq, A. and H. Piater, J. (2008). ONLINE LEARNING OF GAUSSIAN MIXTURE MODELS - A Two-Level Approach. In Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: OPRMLT; ISBN 978-989-8111-21-0; ISSN 2184-4321, SciTePress, pages 605-611. DOI: 10.5220/0001090506050611

@conference{oprmlt08,
author={Arnaud Declercq. and Justus {H. Piater}.},
title={ONLINE LEARNING OF GAUSSIAN MIXTURE MODELS - A Two-Level Approach},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: OPRMLT},
year={2008},
pages={605-611},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001090506050611},
isbn={978-989-8111-21-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: OPRMLT
TI - ONLINE LEARNING OF GAUSSIAN MIXTURE MODELS - A Two-Level Approach
SN - 978-989-8111-21-0
IS - 2184-4321
AU - Declercq, A.
AU - H. Piater, J.
PY - 2008
SP - 605
EP - 611
DO - 10.5220/0001090506050611
PB - SciTePress