14.2.8 Mixture Models, Mixed Pixels

Chapter Contents (Back)
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Elsevier DOI 0905
Blind source separation (BSS); Independent component analysis (ICA); Linear autocorrelation; Nonlinear autocorrelation BibRef

Permuter, H.H.[Haim H.], Francos, J.M.[Joseph M.], Jermyn, I.H.[Ian H.],
A study of Gaussian mixture models of color and texture features for image classification and segmentation,
PR(39), No. 4, April 2006, pp. 695-706.
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Image classification; Texture; Color; Gaussian mixture models; Expectation maximization; k-means; Background model; Decision fusion; Aerial images BibRef

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Gaussian mixture model; EM; Classifier; Confidence; Highest density region BibRef

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VISP(153), No. 3, June 2006, pp. 349-356.
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Springer DOI 0610
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IEEE DOI 0707
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IEEE DOI 0210
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IEEE DOI 0903
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Simplifying Mixture Models Using the Unscented Transform,
PAMI(30), No. 8, August 2008, pp. 1496-1502.
IEEE DOI 0806
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Rotem, O.[Omer], Greenspan, H.K.[Hayit K.], Goldberger, J.[Jacob],
Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework,
CVPR07(1-8).
IEEE DOI 0706
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Reddy, C.K.[Chandan K.], Chiang, H.D.[Hsiao-Dong], Rajaratnam, B.[Bala],
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IEEE DOI 0806
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Omachi, S.[Shinichiro], Omachi, M.[Masako], Aso, H.[Hirotomo],
An Approximation Method of the Quadratic Discriminant Function and Its Application to Estimation of High-Dimensional Distribution,
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An Algorithm for Estimating Mixture Distribution of High Dimensional Vectors and its Application to Character Recognition,
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Liang, Z., Wang, S.,
An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis,
MedImg(28), No. 2, February 2009, pp. 297-310.
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Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.,
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IEEE DOI 0909
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Yamada, M.[Makoto], Sugiyama, M.[Masashi],
Direct Importance Estimation with Gaussian Mixture Models,
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Elsevier DOI 1001
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Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach,
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Online learning; Kernel density estimation; Mixture models; Unlearning; Compression; Hellinger distance; Unscented transform BibRef

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Mei, S.H.[Shao-Hui], He, M.Y.[Ming-Yi], Wang, Z.Y.[Zhi-Yong],
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IEEE DOI 1011
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IEEE DOI 1011
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Annealed SMC Samplers for Dirichlet Process Mixture Models,
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IEEE DOI 1008
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Xie, C.H.[Cong-Hua], Song, Y.Q.[Yu-Qing], Chen, J.M.[Jian-Mei],
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IEEE DOI 1110
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Beta mixture models and the application to image classification,
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IEEE DOI 0911
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Taghia, J., Ma, Z.Y.[Zhan-Yu], Leijon, A.[Arne],
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IEEE DOI 1408
Approximation methods BibRef

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Bayesian estimation BibRef

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Possibilistic clustering; Finite mixture models BibRef

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Gaussian Process-Mixture Conditional Heteroscedasticity,
PAMI(36), No. 5, May 2014, pp. 888-900.
IEEE DOI 1405
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IEEE DOI 1201
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IEEE DOI 1203
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Franczak, B.C., Browne, R.P., McNicholas, P.D.,
Mixtures of Shifted Asymmetric Laplace Distributions,
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IEEE DOI 1406
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Elsevier DOI 1305
Cluster analysis; EM algorithm; Evolutionary algorithms; Finite mixture models; Model-based clustering BibRef

Tits, L., Somers, B., Coppin, P.,
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IEEE DOI 1205
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Cluster analysis; EM algorithm; Gaussian mixture model; Robust EM; Initialization; Number of clusters BibRef

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Classification; Prior distribution; Generalized Gaussian scale mixture; Likelihood function BibRef

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Elsevier DOI 1210
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IEEE DOI 0911
Expectation maximization; Random swap EM; Gaussian mixture model; Split and merge EM; Genetic-based EM; Data clustering BibRef

Fränti, P.[Pasi], Rezaei, M.[Mohammad], Zhao, Q.P.[Qin-Pei],
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Elsevier DOI 1406
Clustering BibRef

Fränti, P.[Pasi], Rezaei, M.[Mohammad],
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IEEE DOI 1212
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Ali, A.M.[Asem M.], Farag, A.A., Alajlan, N., Farag, A.A.,
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IET-CV(6), No. 6, 2012, pp. 524-539.
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Ali, A.M.[Asem M.], Farag, A.A.[Amal A.], Farag, A.A.[Aly A.],
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computer vision BibRef

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IEEE DOI 0505
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IEEE DOI 0409
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Cui, J., Li, X., Zhao, L.,
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IEEE DOI 1307
Convex geometry; endmember extraction; nonnegative matrix factorization (NMF) BibRef

Zhang, T.Z.[Tian-Zhu], Liu, S.[Si], Xu, C.S.[Chang-Sheng], Lu, H.Q.[Han-Qing],
M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling,
PR(46), No. 10, October 2013, pp. 2711-2723.
Elsevier DOI 1306
Scene understanding; Maximum margin clustering; Multiple instance learning (MIL); Gaussian Mixture Model (GMM); Constrained Concave-Convex Procedure (CCCP) BibRef

Fan, W.[Wentao], Bouguila, N.[Nizar],
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Elsevier DOI 1306
Infinite mixture models; Dirichlet process; Generalized Dirichlet; Feature selection; Clustering; Images categorization; Image auto-annotation BibRef

Fan, W.[Wentao], Bouguila, N.[Nizar],
Dynamic Textures Clustering Using a Hierarchical Pitman-Yor Process Mixture of Dirichlet Distributions,
ICIP15(296-300)
IEEE DOI 1512
Dirichlet distribution BibRef

Song, T.C.[Tie-Cheng], Li, H.L.[Hong-Liang],
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Image descriptor; Wavelet decomposition; Local binary pattern (LBP); Gaussian mixture model (GMM); Image classification BibRef

Hara, K., Inoue, K.[Kohei], Urahama, K.[Kiichi],
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IEEE DOI 1307
blind source separation BibRef

Roy, A.[Anandarup], Parui, S.K.[Swapan K.],
Pair-copula based mixture models and their application in clustering,
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Pair-copula construction BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
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Elsevier DOI 1402
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Li, D.[Dawei], Xu, L.[Lihong], Goodman, E.[Erik],
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Chai, J.[Jing], Chen, H.T.[Hong-Tao], Huang, L.X.[Li-Xia], Shang, F.H.[Fan-Hua],
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Hu, L.X.[Li-Xia],
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IEEE DOI 1403
Correlation BibRef

Qu, Q., Nasrabadi, N.M., Tran, T.D.,
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IEEE DOI 1403
Data models BibRef

Palazón-González, V.[Vicente], Marzal, A.[Andrés], Vilar, J.M.[Juan Miguel],
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Elsevier DOI 1404
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Cyclic Linear Hidden Markov Models for Shape Classification,
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Springer DOI 0712
Hidden Markov models BibRef

Palazón-González, V.[Vicente], Marzal, A.[Andrés],
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Springer DOI 0706
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Kersten, J.[Jens],
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Elsevier DOI 1405
Gaussian mixture models BibRef

Zhang, R., Gong, W., Grzeda, V., Yaworski, A., Greenspan, M.,
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IEEE DOI 1406
Cameras BibRef

Bot, R.I.[Radu Ioan], Hendrich, C.[Christopher],
Convergence Analysis for a Primal-Dual Monotone + Skew Splitting Algorithm with Applications to Total Variation Minimization,
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IEEE DOI 1407
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Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization,
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IEEE DOI 1008
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Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucas-Dias, J.M., Li, X.,
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IEEE DOI 1407
Hyperspectral imaging BibRef

Cong-Hua, X.[Xie], Jin-Yi, C.[Chang], Wen-Bin, X.[Xu],
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IET-IPR(8), No. 8, August 2014, pp. 464-476.
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Bayes methods BibRef

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High-dimensional parameter estimation BibRef

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IEEE DOI 1410
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Costa, M., Koivunen, V., Poor, H.V.,
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Gaussian processes BibRef

Huang, S.M.[Shih-Ming], Chou, Y.T.[Yang-Ting], Yang, J.F.[Jar-Ferr],
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Gaussian processes BibRef

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hyperspectral imaging BibRef

Zhou, X.[Xin], Peng, R.[Rongkun], Wang, C.[Congqing],
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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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Model selection BibRef

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IEEE DOI 1602
Gaussian processes BibRef

Imbiriba, T., Moreira Bermudez, J.C., Richard, C.,
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IEEE DOI 1704
Coherence BibRef

Li, H.C., Krylov, V.A., Fan, P.Z., Zerubia, J.B., Emery, W.J.,
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GeoRS(54), No. 4, April 2016, pp. 2153-2170.
IEEE DOI 1604
Image resolution BibRef

Ma, L., Chen, J., Zhou, Y., Chen, X.,
Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect,
GeoRS(54), No. 5, May 2016, pp. 2873-2886.
IEEE DOI 1604
geophysical techniques BibRef

Shi, C., Wang, L.,
Linear Spatial Spectral Mixture Model,
GeoRS(54), No. 6, June 2016, pp. 3599-3611.
IEEE DOI 1606
hyperspectral imaging BibRef

Nielsen, F., Sun, K.,
Guaranteed Bounds on the Kullback-Leibler Divergence of Univariate Mixtures,
SPLetters(23), No. 11, November 2016, pp. 1543-1546.
IEEE DOI 1609
Gaussian processes BibRef

Yerebakan, H.Z.[Halid Ziya], Dundar, M.[Murat],
Partially collapsed parallel Gibbs sampler for Dirichlet process mixture models,
PRL(90), No. 1, 2017, pp. 22-27.
Elsevier DOI 1704
Dirichlet process BibRef

Salvadori, C.[Claudio], Petracca, M.[Matteo], del Rincon, J.M.[Jesus Martinez], Velastin, S.A.[Sergio A.], Makris, D.[Dimitrios],
An optimisation of Gaussian mixture models for integer processing units,
RealTimeIP(13), No. 2, June 2017, pp. 273-289.
Springer DOI 1708
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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

Denisova, A.[Anna], Juravel, Y.[Yuliya], Myasnikov, V.[Vladislav],
Linear Spectral Mixture Analysis of Hyperspectral Images with Atmospheric Distortions,
ICCVG16(134-141).
Springer DOI 1611
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Kim, H.J.[Hyunwoo J.], Adluru, N.[Nagesh], Banerjee, M.[Monami], Vemuri, B.C.[Baba C.], Singh, V.[Vikas],
Interpolation on the Manifold of K Component GMMs,
ICCV15(2884-2892)
IEEE DOI 1602
Gaussian mixture model (GMM) representation of the PDF BibRef

Wu, H.[Hao], Prasad, S.[Saurabh], Priya, T.[Tanu],
Detecting new classes via infinite warped mixture models for hyperspectral image analysis,
ICIP14(5027-5031)
IEEE DOI 1502
Adaptation models 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.[Antonio],
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)
IEEE DOI 1409
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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Vanek, J.[Jan], Machlica, L.[Lukáš], Psutka, J.V.[Josef V.],
Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition,
CIARP13(I:49-56).
Springer DOI 1311
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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).
WWW Link. 1302
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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).
WWW Link. 1302
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Nielsen, F.[Frank],
Closed-form information-theoretic divergences for statistical mixtures,
ICPR12(1723-1726).
WWW Link. 1302
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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).
WWW Link. 1302
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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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Szlam, A.[Arthur], Guo, Z.H.[Zhao-Hui], Osher, S.J.[Stanley J.],
A split Bregman method for non-negative sparsity penalized least squares with applications to hyperspectral demixing,
ICIP10(1917-1920).
IEEE DOI 1009
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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).
Springer DOI 1009
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Wang, R.B.[Rong-Bo], Hou, C.H.[Chao-Huan], Chen, D.[Dong],
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,
ICIP09(3949-3952).
IEEE DOI 0911
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Barcelos, C.A.Z.[Celia A. Zorzo], Chen, Y.M.[Yun-Mei], Chen, F.[Fuhua],
A soft multiphase segmentation model via Gaussian mixture,
ICIP09(4049-4052).
IEEE DOI 0911
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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).
IEEE DOI 0912
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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,
ACIVS09(66-76).
Springer DOI 0909
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Piccardi, M.[Massimo], Gunes, H.[Hatice], Otoom, A.F.[Ahmed Fawzi],
Maximum-likelihood dimensionality reduction in gaussian mixture models with an application to object classification,
ICPR08(1-4).
IEEE DOI 0812
See also Feature extraction techniques for abandoned object classification in video surveillance. BibRef

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,
ICPR08(1-4).
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Reddy, C.K.[Chandan K.], Rajaratnam, B.[Bala],
Component-wise parameter smoothing for learning mixture models,
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Mansjur, D.S.[Dwi Sianto], Fu, Q.A.[Qi-Ang], Juang, B.H.[Biing Hwang],
Utilizing non-uniform cost learning for active control of inter-class confusion,
ICPR08(1-4).
IEEE DOI 0812
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Mansjur, D.S.[Dwi Sianto], Juang, B.H.[Biing Hwang],
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ICPR08(1-4).
IEEE DOI 0812
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Tang, H.[Hao], Huang, T.S.[Thomas S.],
Boosting Gaussian mixture models via discriminant analysis,
ICPR08(1-4).
IEEE DOI 0812
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Bordes, J.B., Prinet, V.,
Mixture Distributions for Weakly Supervised Classification in Remote Sensing Images,
BMVC08(xx-yy).
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Hou, S.[Shaobo], Galata, A.[Aphrodite],
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CVPR08(1-8).
IEEE DOI 0806
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Corona, E.[Enrique], Nutter, B.[Brian], Mitra, S.[Sunanda],
Optimized data-driven order selection method for Gaussian mixtures on clustering problems,
Southwest10(73-76).
IEEE DOI 1005
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Earlier:
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],
An empirical method for comparing the shape of two Gaussian mixtures,
ICIP10(4269-4272).
IEEE DOI 1009
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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).
Springer DOI 0706
See also Embedded Bernoulli Mixture HMMs for Continuous Handwritten Text Recognition. BibRef

Alabau, V.[Vicente], Casacuberta, F.[Francisco], Vidal, E.[Enrique], Juan, A.[Alfons],
Inference of Stochastic Finite-State Transducers Using N -Gram Mixtures,
IbPRIA07(II: 282-289).
Springer DOI 0706
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Bouguila, N.[Nizar], Ziou, D.[Djemel], Hammoud, R.I.[Riad I.],
A Bayesian Non-Gaussian Mixture Analysis: Application to Eye Modeling,
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Wang, P.[Peng], Kohler, C.[Christian], Verma, R.[Ragini],
Estimating Cluster Overlap on Manifolds and its Application to Neuropsychiatric Disorders,
ComponentAnalysis07(1-6).
IEEE DOI 0706
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Penalver Benavent, A.[Antonio], Escolano Ruiz, F.[Francisco], Saez Martinez, J.M.[Juan M.],
Two Entropy-Based Methods for Learning Unsupervised Gaussian Mixture Models,
SSPR06(649-657).
Springer DOI 0608
BibRef
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).
IEEE DOI 0609
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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).
IEEE DOI 0609
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Chen, D.T.[Da-Tong], Yang, J.[Jie],
Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models,
ICPR06(II: 1078-1081).
IEEE DOI 0609
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Abd-Almageed, W.[Wael], Davis, L.S.[Larry S.],
Density Estimation Using Mixtures of Mixtures of Gaussians,
ECCV06(IV: 410-422).
Springer DOI 0608
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Zhu, Y.N.[Ya-Nong], Fisher, M.H.[Mark H.], Zwiggelaar, R.[Reyer],
Improving ASM Search Using Mixture Models for Grey-Level Profiles,
IbPRIA05(I:292).
Springer DOI 0509
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de Ridder, D., Franc, V.,
Robust subspace mixture models using t-distributions,
BMVC03(xx-yy).
HTML Version. 0409
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Cheung, Y.M.[Yiu-Ming],
A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection,
ICPR04(IV: 633-636).
IEEE DOI 0409
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Sminchisescu, C.[Cristian], Jepson, A.D.[Allan D.],
Variational mixture smoothing for non-linear dynamical systems,
CVPR04(II: 608-615).
IEEE DOI 0408
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Huang, K.[Kun], Ma, Y.[Yi], Vidal, R.,
Minimum Effective Dimension for Mixtures of Subspaces: A Robust GPCA Algorithm and its Applications,
CVPR04(II: 631-638).
IEEE DOI 0408
See also Generalized Principal Component Analysis (GPCA). See also Motion segmentation with missing data using powerfactorization and GPCA. BibRef

Vermaak, J., Doucet, A., Perez, P.,
Maintaining multi-modality through mixture tracking,
ICCV03(1110-1116).
IEEE DOI 0311
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Antoniuk, K.[Konstiantyn], Franc, V.[Vojtech], Hlavac, V.[Vaclav],
Learning Markov Networks by Analytic Center Cutting Plane Method,
ICPR12(2250-2253).
WWW Link. 1302
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Franc, V.[Vojtech], Hlavác, V.[Václav],
A Contribution to the Schlesinger's Algorithm Separating Mixtures of Gaussians,
CAIP01(169 ff.).
Springer DOI 0210
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Niemistö, A., Lukin, V.V., Shmulevich, I., Yli-Harja, O.[Olli], Dolia, A.,
A Training-based Optimization Framework for Misclassification Correction,
SCIA01(O-W2). 0206
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Xuan, G., Zhang, W., Chai, P.,
EM Algorithms of Gaussian Mixture Model and Hidden Markov Model,
ICIP01(I: 145-148).
IEEE DOI 0108
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Kudo, M., Imai, H., Shimbo, M.,
A Histogram-based Classifier on Overlapped Bins,
ICPR00(Vol II: 29-33).
IEEE DOI 0009
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Hammoud, R., Mohr, R.,
Mixture Densities for Video Objects Recognition,
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,
ICPR00(Vol II: 610-613).
IEEE DOI 0009
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Somol, P., Grim, J.[Jiri], Novovicova, J.[Jana], Pudil, P.[Pavel], Ferri, F.J.[Francesc J.],
Initializing Normal Mixtures of Densities,
ICPR98(Vol I: 886-890).
IEEE DOI 9808
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Kudo, M.[Mineichi], Shimbo, M.[Masaru], Sumiyoshi, S.[Satoru], Tenmoto, H.[Hiroshi],
A Subclass-Based Mixture Model for Pattern Recognition,
ICPR98(Vol I: 870-872).
IEEE DOI 9808
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Schultz, N.[Nette], Carstensen, J.M.[Jens Michael],
Bimodal histogram transformation based on maximum likelihood parameter estimates in univariate Gaussian mixtures,
CIAP97(II: 532-543).
Springer DOI 9709
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixed Pixels, Unmixing .


Last update:Nov 11, 2017 at 13:31:57