Skip to search formSkip to main content>Semantic Scholar Semantic Scholar's Logo

Search

You are currently offline. Some features of the site may not work correctly.

Semantic Scholar uses AI to extract papers important to this topic.

Highly Cited

2008

Highly Cited

2008

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

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… Expand

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… Expand

Highly Cited

2002

Highly Cited

2002

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

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… Expand

Highly Cited

2000

Highly Cited

2000

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

Highly Cited

1999

Highly Cited

1999

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

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… Expand

Highly Cited

1995

Highly Cited

1995

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

Highly Cited

1988

Highly Cited

1988

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