14.2.8 Mixture Models, Mixed Pixels

Chapter Contents (Back)
Mixed Pixels. Mixture Models. 9905
Hyperspectral specific:
See also Hyperspectral Mixture Models, Mixed Pixels.

Lawoko, C.R.O., McLachlan, G.J.,
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And: Extensions:
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Biernacki, C.[Christophe], Celeux, G.[Gilles], Govaert, G.[Gerard],
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Kitamoto, A.[Asanbou], Takagi, M.[Mikio],
Image Classification using Probabilistic Models that Reflect the Internal Structure of Mixels,
PAA(2), No. 1, 1999, pp. 31-43. BibRef 9900
Earlier:
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ICPR96(II: 745-749).
IEEE DOI 9608
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Cootes, T.F., Taylor, C.J.,
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Boshra, M.[Michael], Zhang, H.[Hong],
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Aguiar, A.P.D., Shimabukuro, Y.E., Mascarenhas, N.D.A.,
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Tadjudin, S., Landgrebe, D.A.,
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Lee, T.W.[Te-Won], Lewicki, M.S.[Michael S.], Sejnowski, T.J.[Terrence J.],
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Modeling classes as linear combinations of independent, non-Gaussian densities. BibRef

Lee, T.W.[Te-Won], Lewicki, M.S.,
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Park, H.J.[Hyun-Jin], Lee, T.W.[Te-Won],
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Carreira-Perpińán, M.Á.[Miguel Á.],
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Carreira-Perpińán, M.Á.[Miguel Á.], Williams, C.K.I.[Christopher K.I.],
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Carreira-Perpinan, M.A.[Miguel A.],
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Earlier:
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See also Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition, The. Convergence is fast for very narrow or very wide, but slow for intermediate. Ways to accelerate. BibRef

Carreira-Perpinan, M.A.[Miguel A.],
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Yang, Z.R.[Zheng Rong], Zwolinski, M.[Mark],
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Analyze whether components are independent, if so then the information is important. BibRef

Simon, C., Loubaton, P., Jutten, C.,
Separation of a Class of Convolutive Mixtures: A Contrast Function Approach,
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Dahmen, J.[Jörg], Keysers, D.[Daniel], Ney, H.[Hermann], Güld, M.O.[Mark Oliver],
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Earlier: A1, A2, A4, A3:
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Keysers, D.[Daniel], Macherey, W.[Wolfgang], Ney, H.[Hermann], Dahmen, J.[Jorg],
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Rand, R.S., Keenan, D.M.,
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Collins, E.F., Roberts, D.A., Borel, C.C.,
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Yang, X.Y.[Xiang-Yu], Liu, J.[Jun],
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Figueiredo, M.A.T., Jain, A.K.,
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PAMI(24), No. 3, March 2002, pp. 381-396.
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Earlier:
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Figueiredo, M.A.T.,
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Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.,
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Add Feature Salience in clustering method and an EM algorithm to estimate it. Salience of irrelevant features goes to 0, thus de-selecting them. BibRef

Dattatreya, G.R.,
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And:
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Dattatreya, G.R.,
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Dattatreya, G.R., Fang, X.R.F.[Xiao-Ri Frank],
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Aylward, S.R.[Stephen R.],
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Govaert, G.[Gérard], Nadif, M.[Mohamed],
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Govaert, G.[Gerard], Nadif, M.[Mohamed],
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Earlier:
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CVPR99(I: 416-422).
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Earlier:
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Perina, A.[Alessandro], Jojic, N.[Nebojsa], Cristani, M.[Marco], Murino, V.[Vittorio],
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Jojic, N.[Nebojsa], Petrovic, N.[Nemanja], Frey, B.J.[Brendan J.], Huang, T.S.[Thomas S.],
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Bouguila, N.[Nizar], Ziou, D.[Djemel],
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Multinomial Dirichlet; Finite mixture models; Maximum likelihood; EM; Semantic features; Image retrieval; Vistex; Cooccurrence matrix BibRef

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Discrete data; Finite mixture models; Multinomial; Generalized Dirichlet distribution; EM; Spatial color; Image databases; Labeled and unlabeled images; Summarization; Text classification BibRef

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Bouguila, N.[Nizar],
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Boutemedjet, S.[Sabri], Bouguila, N.[Nizar], Ziou, D.[Djemel],
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Earlier:
Unsupervised Feature and Model Selection for Generalized Dirichlet Mixture Models,
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Mixture models; Generalized Gaussian; Feature selection; Nonparametric Bayes; MCMC; Gibbs sampling; Photographic; Painting; Segmentation; Infrared images BibRef

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CVIU(117), No. 12, 2013, pp. 1659-1671.
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Infrared
See also Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection. BibRef

Song, Z.Y.[Zi-Yang], Ali, S.[Samr], Bouguila, N.[Nizar],
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IET-IPR(14), No. 11, September 2020, pp. 2321-2332.
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Earlier:
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Springer DOI 1909
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El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
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Asymmetric Gaussian distribution BibRef

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Discrete features; Finite mixture models; Multinomial; Dirichlet; Generalized Dirichlet; Leave-one-out likelihood; SVM; Generative/discriminative; Scene classification; Visual words BibRef

Jin, H.D.[Hui-Dong], Leung, K.S.[Kwong-Sak], Wong, M.L.[Man-Leung], Xu, Z.B.[Zong-Ben],
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Aiyer, A.[Anuradha], Pyun, K.P.[Kyungsuk Peter], Huang, Y.Z.[Ying-Zong], O'Brien, D.B.[Deirdre B.], Gray, R.M.[Robert M.],
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Pernkopf, F.[Franz], Bouchaffra, D.[Djamel],
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Zhang, B.B.[Bai-Bo], Zhang, C.S.[Chang-Shui], Yi, X.[Xing],
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Ma, J.W.[Jin-Wen], Fu, S.Q.[Shu-Qun],
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Haertel, V.F., Shimabukuro, Y.E.,
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IEEE DOI 0512
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Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization,
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expectation-maximisation algorithm BibRef

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ACPR13(361-365)
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Gaussian processes BibRef

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Elsevier DOI 1505
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Image resolution BibRef

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Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect,
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geophysical techniques BibRef

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Elsevier DOI 1704
Dirichlet process BibRef

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Springer DOI 1708
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Wasserstein-Distance-Based Gaussian Mixture Reduction,
SPLetters(25), No. 10, October 2018, pp. 1465-1469.
IEEE DOI 1810
approximation theory, Gaussian processes, mixture models, signal processing, signal processing applications, Wasserstein distance (WD) BibRef

Marinoni, A., Plaza, J., Plaza, A., Gamba, P.,
Estimating Nonlinearities in p-Linear Hyperspectral Mixtures,
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IEEE DOI 1811
Hyperspectral imaging, Mixture models, Image reconstruction, Measurement, Earth, Adaptive fitting, nonorthogonal projection BibRef

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PR(87), 2019, pp. 269-284.
Elsevier DOI 1812
Finite mixture models, Generalized Gamma distribution, Variational expectation-maximization (VEM), Extended factorized approximation BibRef

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DOI Link 1812
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Mukhopadhaya, S.[Sayan], Kumar, A.[Anil], Stein, A.[Alfred],
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He, D.[Da], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Spatiotemporal Subpixel Geographical Evolution Mapping,
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IEEE DOI 1904
ecology, environmental monitoring (geophysics), geophysical image processing, image classification, subpixel mapping (SPM) BibRef

Zhu, D.H.[De-Hui], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Single-Spectrum-Driven Binary-Class Sparse Representation Target Detector for Hyperspectral Imagery,
GeoRS(59), No. 2, February 2021, pp. 1487-1500.
IEEE DOI 2101
Dictionaries, Detectors, Training, Hyperspectral imaging, Object detection, Classification algorithms, Binary hypothesis, target detection BibRef

Safont, G.[Gonzalo], Salazar, A.[Addisson], Vergara, L.[Luis], Gómez, E.[Enriqueta], Villanueva, V.[Vicente],
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PR(93), 2019, pp. 312-323.
Elsevier DOI 1906
Dynamic modeling, Non-Gaussian mixtures, ICA, HMM, EEG BibRef

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Zhao, Y., Shrivastava, A.K., Tsui, K.L.,
Regularized Gaussian Mixture Model for High-Dimensional Clustering,
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IEEE DOI 1907
Covariance matrices, Correlation, Maximum likelihood estimation, Feature extraction, Gaussian mixture model, unsupervised learning BibRef

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Estimation of Gaussian mixture models via tensor moments with application to online learning,
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Method of moments, Alternating gradient descent, Online learning, Tensor analysis BibRef

Delon, J.[Julie], Desolneux, A.[Agnčs],
A Wasserstein-Type Distance in the Space of Gaussian Mixture Models,
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IEEE DOI 2008
Feature extraction, Gaussian mixture model, Task analysis, Information processing, Sun, Image color analysis, hybrid gaussian mixture model BibRef

Manouchehri, N.[Narges], Bouguila, N.[Nizar], Fan, W.T.[Wen-Tao],
Nonparametric variational learning of multivariate beta mixture models in medical applications,
IJIST(31), No. 1, 2021, pp. 128-140.
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batch variational learning, Dirichlet process mixtures, medical applications, mixture models, online variational learning BibRef

Manouchehri, N.[Narges], Kalra, M.[Meeta], Bouguila, N.[Nizar],
Online variational inference on finite multivariate Beta mixture models for medical applications,
IET-IPR(15), No. 9, 2021, pp. 1869-1882.
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Asheri, H.[Hadi], Hosseini, R.[Reshad], Araabi, B.N.[Babak Nadjar],
A new EM algorithm for flexibly tied GMMs with large number of components,
PR(114), 2021, pp. 107836.
Elsevier DOI 2103
Gaussian mixture model, Parameter sharing, Tied GMM, Computation factorization and reduction, Newton method, Clustering BibRef

Ge, C.Y.[Chen-Yu], Wang, M.M.[Meng-Meng], Zhang, H.M.[Hong-Ming], Chen, H.[Huan], Sun, H.G.[Hong-Guang], Chang, Y.[Yi], Yang, Q.[Qinke],
A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1,
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Chasani, P.[Paraskevi], Likas, A.[Aristidis],
The UU-test for statistical modeling of unimodal data,
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Elsevier DOI 2112
Unimodal data, Unimodality test, Statistical modeling, Uniform mixture model BibRef

Xie, J.Y.[Ji-Yang], Ma, Z.Y.[Zhan-Yu], Xue, J.H.[Jing-Hao], Zhang, G.Q.[Guo-Qiang], Sun, J.[Jian], Zheng, Y.[Yinhe], Guo, J.[Jun],
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition,
IP(30), 2021, pp. 9208-9219.
IEEE DOI 2112
Uncertainty, Feature extraction, Image recognition, Stochastic processes, Optimization, Bayes methods, mixture of gaussian mixture models BibRef

Giry Fouquet, E.[Erwan], Fauvel, M.[Mathieu], Mallet, C.[Clément],
Fast estimation for robust supervised classification with mixture models,
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Elsevier DOI 2112
Classification, Label noise, ADMM, Convex optimization, Clustering BibRef

Li, Y.S.[Yun-Song], Shi, Y.Z.[Yan-Zi], Wang, K.[Keyan], Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Gamba, P.[Paolo],
Target Detection With Unconstrained Linear Mixture Model and Hierarchical Denoising Autoencoder in Hyperspectral Imagery,
IP(31), 2022, pp. 1418-1432.
IEEE DOI 2202
Detectors, Object detection, Hyperspectral imaging, Noise reduction, Mixture models, Interference, Deep learning, hyperspectral target detection BibRef

Liu, Z.Q.[Zi-Quan], Yu, L.[Lei], Hsiao, J.H.[Janet H.], Chan, A.B.[Antoni B.],
PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models,
PAMI(44), No. 6, June 2022, pp. 3197-3211.
IEEE DOI 2205
Manifolds, Hidden Markov models, Kernel, Data models, Probabilistic logic, Approximation algorithms, Analytical models, probabilistic models BibRef

Jeon, Y.[Younghwan], Hwang, G.[Ganguk],
Bayesian mixture of gaussian processes for data association problem,
PR(127), 2022, pp. 108592.
Elsevier DOI 2205
Gaussian processes, Bayesian models, Variational inference, Expectation maximization BibRef

Seo, D.M.[Dong-Min], Oh, S.[Sangwoo], Lee, D.[Daekyeom],
Classification and Identification of Spectral Pixels with Low Maritime Occupancy Using Unsupervised Machine Learning,
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Melnykov, V.[Volodymyr], Wang, Y.[Yang],
Conditional mixture modeling and model-based clustering,
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Elsevier DOI 2210
finite mixture model, model-based clustering, non-compact clusters, regression, variable selection BibRef

Yu, Q.Q.[Qi-Qiong], Cao, G.[Guo], Shi, H.[Hao], Zhang, Y.Q.[You-Qiang], Fu, P.[Peng],
EPLL image restoration with a bounded asymmetrical Student's-t mixture model,
JVCIR(88), 2022, pp. 103611.
Elsevier DOI 2210
EPLL image restoration, Finite mixture model, Bounded asymmetrical Student's-t mixture model, Regularization parameters BibRef

Chen, C.[Chen], Tang, M.J.[Meng-Jiao], Rong, Y.[Yao],
Detection of a Rare Multichannel Gaussian Signal via Higher Criticism,
SPLetters(29), 2022, pp. 2063-2067.
IEEE DOI 2211
Detectors, Sparse matrices, Mixture models, Signal detection, Eigenvalues and eigenfunctions, Data models, asymptotic optimality BibRef

Bonnaire, T.[Tony], Decelle, A.[Aurélien], Aghanim, N.[Nabila],
Regularization of Mixture Models for Robust Principal Graph Learning,
PAMI(44), No. 12, December 2022, pp. 9119-9130.
IEEE DOI 2212
Mixture models, Convergence, Symmetric matrices, Manifolds, Computational modeling, Topology, Optimization, manifold learning BibRef

Fan, W.T.[Wen-Tao], Yang, L.[Lin], Bouguila, N.[Nizar],
Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions,
PAMI(44), No. 12, December 2022, pp. 9654-9668.
IEEE DOI 2212
Data models, Bayes methods, Analytical models, Mixture models, Modeling, Tools, Task analysis, Axial data, Watson distribution, depth image BibRef

Butt, J.A.[Jemil Avers], Salido-Monzú, D.[David], Wieser, A.[Andreas],
Phase ambiguity resolution and mixed pixel detection in EDM with multiple modulation wavelengths,
PandRS(198), 2023, pp. 255-271.
Elsevier DOI 2304
Electro-optical distance measurement (EDM), Phase measurement, Ambiguity resolution, Mixed pixel, Mixed integer linear programming BibRef

Castella, M.[Marc],
Unsupervised Linear Component Analysis for a Class of Probability Mixture Models,
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Stochastic first-order learning for large-scale flexibly tied Gaussian mixture models,
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Elsevier DOI 2402
Gaussian mixture models, First-order optimization, Manifold optimization BibRef


Zach, M.[Martin], Pock, T.[Thomas], Kobler, E.[Erich], Chambolle, A.[Antonin],
Explicit Diffusion of Gaussian Mixture Model Based Image Priors,
SSVM23(3-15).
Springer DOI 2307
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Ye, F.[Fei], Bors, A.G.[Adrian G.],
Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process,
ICCV21(10675-10684)
IEEE DOI 2203
BibRef
Earlier:
Lifelong learning of interpretable image representations,
IPTA20(1-6)
IEEE DOI 2206
Analytical models, Adaptation models, Codes, Computational modeling, Mixture models, Network architecture, Representation learning. Learning systems, Interpolation, Databases, Image synthesis, Tools, Generators, Mutual information, Lifelong learning, Mutual information BibRef

Gelvez, T.[Tatiana], Bacca, J.[Jorge], Arguello, H.[Henry],
Interpretable Deep Image Prior Method Inspired in Linear Mixture Model for Compressed Spectral Image Recovery,
ICIP21(1934-1938)
IEEE DOI 2201
Training, Image segmentation, Image coding, Neural networks, Imaging, Mixture models, Silicon, Spectral image, Deep image prior, Linear mixture model BibRef

La Grassa, R.[Riccardo], Gallo, I.[Ignazio], Vetro, C.[Calogero], Landro, N.[Nicola],
Learning to Navigate in the Gaussian Mixture Surface,
CAIP21(I:414-423).
Springer DOI 2112
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Zwicker, M.[Matthias], Hu, Q.Y.[Qi-Yang], Szabó, A.[Attila], Portenier, T.[Tiziano], Favaro, P.[Paolo],
Disentangling Factors of Variation by Mixing Them,
CVPR18(3399-3407)
IEEE DOI 1812
Training, Decoding, Labeling, Image coding, Image representation, Image color analysis BibRef

Kolouri, S., Rohde, G.K., Hoffmann, H.,
Sliced Wasserstein Distance for Learning Gaussian Mixture Models,
CVPR18(3427-3436)
IEEE DOI 1812
Radon, Transforms, Kernel, Gaussian mixture model, Machine learning BibRef

Yu, X., Liu, T., Gong, M., Batmanghelich, K., Tao, D.,
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption,
CVPR18(4480-4489)
IEEE DOI 1812
Estimation, Kernel, Convergence, Semisupervised learning, Training data, Quadratic programming, Noise measurement BibRef

Pu, C., Li, N., Tylecek, R., Fisher, B.,
DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment,
3DV18(766-774)
IEEE DOI 1812
Gaussian processes, image registration, probability, statistical distributions, uncertainty model, invariant GMM, uncertainty estimation BibRef

Martins, I.[Isabel], Carvalho, P.[Pedro], Corte-Real, L.[Luís], Alba-Castro, J.L.[José Luis],
BMOG: Boosted Gaussian Mixture Model with Controlled Complexity,
IbPRIA17(50-57).
Springer DOI 1706
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Mezuman, E., Weiss, Y.,
A tight convex upper bound on the likelihood of a finite mixture,
ICPR16(1683-1688)
IEEE DOI 1705
Computational modeling, Convex functions, Data models, Entropy, Mathematical model, Mixture models, Upper, bound BibRef

Wan, Y.C.[Yu-Chai], Liu, X.B.[Xia-Bi], Tang, Y.Y.[Yu-Yang],
Simplifying Gaussian mixture model via model similarity,
ICPR16(3180-3185)
IEEE DOI 1705
Computational modeling, Gaussian mixture model, Linear programming, Merging, Mixture models, Neurons BibRef

Lee, S.X., Leemaqz, K.L., McLachlan, G.J.,
A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models,
DICTA16(1-8)
IEEE DOI 1701
Algorithm design and analysis BibRef

Wilhelm, T., Wohler, C.,
Flexible Mixture Models for Colour Image Segmentation of Natural Images,
DICTA16(1-7)
IEEE DOI 1701
Bayes methods BibRef

Chamroukhi, F.[Faicel], Bartcus, M.[Marius], Glotin, H.[Herve],
Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering,
ICPR14(1460-1465)
IEEE DOI 1412
Adaptation models BibRef

Tsuchiya, C.[Chikao], Malisiewicz, T.[Tomasz], Torralba, A.B.[Antonio B.],
Exemplar Network: A Generalized Mixture Model,
ICPR14(598-603)
IEEE DOI 1412
Big data BibRef

Guillemot, T.[Thierry], Almansa, A.[Andres], Boubekeur, T.[Tamy],
Covariance Trees for 2D and 3D Processing,
CVPR14(556-563)
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Gaussian Mixture Models. Statistical image processing. bayesian a posteriori BibRef

Psutka, J.V.[Josef V.],
Gaussian Mixture Model Selection Using Multiple Random Subsampling with Initialization,
CAIP15(I:678-689).
Springer DOI 1511
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Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition,
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Frouzesh, F.[Faezeh], Pledger, S.[Shirley], Hirose, Y.[Yuichi],
A combined method for finding best starting points for optimisation in bernoulli mixture models,
ICPR12(1128-1131).
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Tsuboshita, Y.[Yukihiro], Kato, N.[Noriji], Fukui, M.[Motofumi], Okada, M.[Masato],
Image annotation using adapted Gaussian mixture model,
ICPR12(1346-1350).
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Nielsen, F.[Frank],
Closed-form information-theoretic divergences for statistical mixtures,
ICPR12(1723-1726).
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Schwander, O.[Olivier], Schutz, A.J.[Aurelien J.], Nielsen, F.[Frank], Berthoumieu, Y.[Yannick],
k-MLE for mixtures of generalized Gaussians,
ICPR12(2825-2828).
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Elnakib, A.[Ahmed], Gimel'farb, G.L.[Georgy L.], Inanc, T.[Tamer], El-Baz, A.[Ayman],
Modified Akaike information criterion for estimating the number of components in a probability mixture model,
ICIP12(2497-2500).
IEEE DOI 1302
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Evangelio, R.H.[Ruben Heras], Patzold, M.[Michael], Sikora, T.[Thomas],
Splitting Gaussians in Mixture Models,
AVSS12(300-305).
IEEE DOI 1211
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Li, B.[Bo], Liu, W.J.[Wen-Ju], Dou, L.H.[Li-Hua],
Learning GMM Using Elliptically Contoured Distributions,
ICPR10(511-514).
IEEE DOI 1008
Gaussian mixture model BibRef

Ji, Y.F.[Yang-Feng], Lin, T.[Tong], Zha, H.B.[Hong-Bin],
CDP Mixture Models for Data Clustering,
ICPR10(637-640).
IEEE DOI 1008
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Nielsen, F.[Frank], Boltz, S.[Sylvain], Schwander, O.[Olivier],
Bhattacharyya Clustering with Applications to Mixture Simplifications,
ICPR10(1437-1440).
IEEE DOI 1008
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Martinez-Uso, A.[Adolfo], Pla, F.[Filiberto], Sotoca, J.M.[Jose M.],
A Semi-supervised Gaussian Mixture Model for Image Segmentation,
ICPR10(2941-2944).
IEEE DOI 1008
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Nacereddine, N.[Nafaa], Tabbone, S.A.[Salavatore A.], Ziou, D.[Djemel], Hamami, L.[Latifa],
Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation,
ICPR10(4557-4560).
IEEE DOI 1008
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Nickisch, H.[Hannes], Rasmussen, C.E.[Carl Edward],
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models,
DAGM10(272-282).
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Blind separation of instantaneous linear mixtures of cyclostationary signals,
IASP10(492-495).
IEEE DOI 1004
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Garcia, V.[Vincent], Nielsen, F.[Frank], Nock, R.[Richard],
Levels of Details for Gaussian Mixture Models,
ACCV09(II: 514-525).
Springer DOI 0909
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Mancera, L., Babacan, S.D.[S. Derin], Molina, R., Katsaggelos, A.K.,
Image restoration by mixture modelling of an overcomplete linear representation,
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IEEE DOI 0911
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Barcelos, C.A.Z.[Celia A. Zorzo], Chen, Y.M.[Yun-Mei], Chen, F.[Fuhua],
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Dey, C.[Chandrama], Jia, X.P.[Xiu-Ping], Fraser, D., Wang, L.,
Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine,
DICTA09(291-295).
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Otoom, A.F.[Ahmed Fawzi], Concha, O.P.[Oscar Perez], Gunes, H.[Hatice], Piccardi, M.[Massimo],
Mixtures of Normalized Linear Projections,
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Piccardi, M.[Massimo], Gunes, H.[Hatice], Otoom, A.F.[Ahmed Fawzi],
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ICPR08(1-4).
IEEE DOI 0812

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Horta, M.M.[Michelle M.], Mascarenhas, N.D.A.[Nelson D. A.], Frery, A.C.[Alejandro C.],
A comparison of clustering fully polarimetric SAR images using SEM algorithm and G0P mixture model with different initializations,
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Mansjur, D.S.[Dwi Sianto], Fu, Q.A.[Qi-Ang], Juang, B.H.[Biing Hwang],
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Tang, H.[Hao], Huang, T.S.[Thomas S.],
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ICPR08(1-4).
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Bordes, J.B., Prinet, V.,
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Corona, E.[Enrique], Nutter, B.[Brian], Mitra, S.[Sunanda],
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Southwest10(73-76).
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Non-parametric Estimation of Mixture Model Order,
Southwest08(145-148).
IEEE DOI 0803
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Santos-Villalobos, H.J.[Hector J.], Boutin, M.[Mireille],
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ICIP10(4269-4272).
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Boutin, M.[Mireille], Comer, M.L.[Mary L.],
Faithful Shape Representation for 2D Gaussian Mixtures,
ICIP07(VI: 369-372).
IEEE DOI 0709
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Romero, V.[Verónica], Giménez, A.[Adriŕ], Juan, A.[Alfons],
Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images,
IbPRIA07(I: 539-546).
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Alabau, V.[Vicente], Casacuberta, F.[Francisco], Vidal, E.[Enrique], Juan, A.[Alfons],
Inference of Stochastic Finite-State Transducers Using N -Gram Mixtures,
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Two Entropy-Based Methods for Learning Unsupervised Gaussian Mixture Models,
SSPR06(649-657).
Springer DOI 0608
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Earlier:
EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models,
ICPR06(II: 451-455).
IEEE DOI 0609
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Lin, B.[Bin], Wang, X.J.[Xian-Ji], Zhong, R.T.[Run-Tian], Zhuang, Z.Q.[Zhen-Quan],
Continuous Optimization based-on Boosting Gaussian Mixture Model,
ICPR06(I: 1192-1195).
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Lu, X.[Xiqun],
Joint Distributions based on DFB and Gaussian Mixtures for Evaluation of Style Similarity among Paintings,
ICPR06(II: 865-868).
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Chen, D.T.[Da-Tong], Yang, J.[Jie],
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ICPR06(II: 1078-1081).
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Abd-Almageed, W.[Wael], Davis, L.S.[Larry S.],
Density Estimation Using Mixtures of Mixtures of Gaussians,
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Zhu, Y.N.[Ya-Nong], Fisher, M.H.[Mark H.], Zwiggelaar, R.[Reyer],
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IbPRIA05(I:292).
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de Ridder, D., Franc, V.,
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Huang, K.[Kun], Ma, Y.[Yi], Vidal, R.,
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CVPR04(II: 631-638).
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See also Generalized Principal Component Analysis (GPCA).
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Vermaak, J., Doucet, A., Perez, P.,
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ICCV03(1110-1116).
IEEE DOI 0311
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ICPR12(2250-2253).
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Franc, V.[Vojtech], Hlavác, V.[Václav],
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CAIP01(169 ff.).
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Niemistö, A., Lukin, V.V., Shmulevich, I., Yli-Harja, O.[Olli], Dolia, A.,
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Kudo, M., Imai, H., Shimbo, M.,
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IEEE DOI 0009
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Hammoud, R., Mohr, R.,
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ICPR00(Vol II: 71-75).
IEEE DOI 0009
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Zwart, J.P., Kröse, B.J.A.,
Constrained Mixture Modeling of Intrinsically Low-dimensional Distributions,
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Somol, P., Grim, J.[Jiri], Novovicova, J.[Jana], Pudil, P.[Pavel], Ferri, F.J.[Francesc J.],
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IEEE DOI 9808
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Kudo, M.[Mineichi], Shimbo, M.[Masaru], Sumiyoshi, S.[Satoru], Tenmoto, H.[Hiroshi],
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Schultz, N.[Nette], Carstensen, J.M.[Jens Michael],
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Mixture Models, Mixed Pixels .


Last update:Mar 16, 2024 at 20:36:19