Results 221 to 230 of about 64,158 (253)

Coding using Gaussian mixture and generalized Gaussian models

Proceedings of 3rd IEEE International Conference on Image Processing, 2002
In transform image coding, the histograms of transform coefficients can be approximately modeled by generalized Gaussian (GG) random variables. However, the GG models may not fit the DC distribution. One approach uses DPCM for the DC data, which greatly complicates bit allocation; another assumes a single Gaussian (SG) model, which may be a poor model.
Jonathan K. Su, Russell M. Mersereau
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Gaussian Mixture Model and Gaussian Supervector for Image Classification

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018
Gaussian Mixture Model (GMM) has been widely used in speech signal and image signal classification tasks. It can be directly used as a classifier, or used as the representation of speech or image signals. Another important usage of GMM is to serve as the Universal Background Model (UBM) to generate speech representations such as Gaussian Supervector ...
Yuechi Jiang, Frank Hung-Fat Leung
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The supervised learning Gaussian mixture model

Journal of Computer Science and Technology, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jiyong Ma, Wen Gao 0001
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Gaussian Mixture Models

2014
In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their ...
Dong Yu, Li Deng
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A symmetrization of the Subspace Gaussian Mixture Model

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Last year we introduced the Subspace Gaussian Mixture Model (SGMM), and we demonstrated Word Error Rate improvements on a fairly small-scale task. Here we describe an extension to the SGMM, which we call the symmetric SGMM. It makes the model fully symmetric between the “speech-state vectors” and “speaker vectors” by making the mixture weights depend ...
Daniel Povey, Martin Karafiát, Arnab Ghoshal, Petr Schwarz   +3 more
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On the number of components in a Gaussian mixture model

WIREs Data Mining and Knowledge Discovery, 2014
Mixture distributions, in particular normal mixtures, are applied to data with two main purposes in mind. One is to provide an appealing semiparametric framework in which to model unknown distributional shapes, as an alternative to, say, the kernel density method.
Geoffrey J. McLachlan, Suren I. Rathnayake   +1 more
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Bounded generalized Gaussian mixture model

Pattern Recognition, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thanh Minh Nguyen 0001, Q. M. Jonathan Wu, Hui Zhang 0015   +2 more
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The Infinite Gaussian Mixture Model.

2000
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components.
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Unsupervised Parameterisation of Gaussian Mixture Models

2002
In this paper we explain a new practical methodology to fully parameterise Gaussian Mixture Models (GMM) to describe data set distributions. Our approach analyses hierarchically a data set distribution to be modeled, determining unsupervisedly an appropriate number of components of the GMM, and their corresponding parameterisation. Results are provided
Daniel Ponsa, F. Xavier Roca
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Multivariate Scale Mixture of Gaussians Modeling

2006
In this paper, we present an approach to generate a class of multivariate probability models, which are referred to as scale mixture of Gaussians models. They are constructed as normal variance mixture models, in which the covariance matrix involves a stochastic scale factor with a given prior distribution.
Torbjørn Eltoft, Taesu Kim, Te-Won Lee
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