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Highly Cited

2021

Highly Cited

2021

Finite mixture models provide a natural way of modeling continuous or discrete outcomes that are observed from populations…

Highly Cited

2009

Highly Cited

2009

Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian…

Highly Cited

2004

Highly Cited

2004

Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient…

Highly Cited

2002

Highly Cited

2002

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective…

Highly Cited

2000

Highly Cited

2000

The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at…

Highly Cited

2000

Highly Cited

2000

Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models…

Highly Cited

1999

Highly Cited

1999

A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or…

Highly Cited

1999

Highly Cited

1999

In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an…

Highly Cited

1995

Highly Cited

1995

This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification…

Highly Cited

1988

Highly Cited

1988

General Introduction Introduction History of Mixture Models Background to the General Classification Problem Mixture Likelihood…