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Splitting Gaussians in Mixture Models
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker ...
Rubén Heras Evangelio +2 more
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Hierarchical Gaussian mixture model
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified.
Vincent Garcia +2 more
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Parsimonious Gaussian mixture models
Statistics and Computing, 2008Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases.
Paul David McNicholas +1 more
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Modelling profiles with a mixture of Gaussians
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be
James Orwell +3 more
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Combining Gaussian Mixture Models
2004A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters ...
Hyoungjoo Lee, Sungzoon Cho
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Bounded generalized Gaussian mixture model
Pattern Recognition, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thanh Minh Nguyen, Q M Jonathan Wu
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Detection in underwater noises modeled as a Gaussian-Gaussian mixture
ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005We study statistical modeling by a Gaussian-Gaussian mixture for two different underwater noise samples. We show that one of them can be adequately described by a Gaussian-Gaussian mixture whereas the other one is very close to a Gaussian model and is described by a mixture with a very small perturbating term.
Michel Bouvet, Stuart C. Schwartz
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Coding using Gaussian mixture and generalized Gaussian models
Proceedings of 3rd IEEE International Conference on Image Processing, 2002In 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), 2018Gaussian 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, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jiyong Ma, Wen Gao 0001
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