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Incremental learning of temporally-coherent Gaussian mixture models

Arandjelovic, Ognjen and Cipolla, R. 2006, Incremental learning of temporally-coherent Gaussian mixture models, Society of Manufacturing Engineers (SME) Technical Papers, vol. TP06PUB22, pp. 1-1.

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Title Incremental learning of temporally-coherent Gaussian mixture models
Author(s) Arandjelovic, Ognjen
Cipolla, R.
Journal name Society of Manufacturing Engineers (SME) Technical Papers
Volume number TP06PUB22
Start page 1
End page 1
Total pages 1
Publisher IEEE
Place of publication Piscataway, New Jersey
Publication date 2006
ISSN 0361-8765
Keyword(s) density
estimation
Gaussian
incremental
mixture
temporal
Summary In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive one- by-one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed which is increased (or reduced) when enough evidence for a new component is seen. This is deducedfrom the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions.
Language eng
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058452

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.