14.2.7 Mixture Models, Mixed Pixels

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Biernacki, C.[Christophe], Celeux, G.[Gilles], Govaert, G.[Gerard],
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Kitamoto, A.[Asanbou], Takagi, M.[Mikio],
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PAA(2), No. 1, 1999, pp. 31-43. BibRef 9900
Earlier:
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ICPR96(II: 745-749).
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Cootes, T.F., Taylor, C.J.,
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IVC(17), No. 8, June 1999, pp. 567-573.
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Boshra, M.[Michael], Zhang, H.[Hong],
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PRL(20), No. 7, July 1999, pp. 689-698. BibRef 9907

Aguiar, A.P.D., Shimabukuro, Y.E., Mascarenhas, N.D.A.,
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Tadjudin, S., Landgrebe, D.A.,
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Martínez, A.M.[Aleix M.], Vitrià, J.[Jordi],
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Brown, M., Lewis, H.G., Gunn, S.R.,
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Lee, T.W.[Te-Won], Lewicki, M.S.[Michael S.], Sejnowski, T.J.[Terrence J.],
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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-Perpiñán, M.Á.[Miguel Á.],
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Carreira-Perpiñán, 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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Acceleration Strategies for Gaussian Mean-Shift Image Segmentation,
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Carreira-Perpinan, M.A.[Miguel A.],
Generalised blurring mean-shift algorithms for nonparametric clustering,
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Heinz, D.C., Chang, C.I.,
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Chang, C.I., Ji, B.,
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Chang, C.I., Ji, B.,
Fisher's Linear Spectral Mixture Analysis,
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Chang, C.I.[Chein-I], Chiang, S.S.[Shao-Shan], Smith, J.A., Ginsberg, I.W.,
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Yang, Z.R.[Zheng Rong], Zwolinski, M.[Mark],
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PAMI(23), No. 4, April 2001, pp. 396-403.
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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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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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Powerful finite mixture model based on the generalized dirichlet distribution: unsupervised learning and applications,
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Multinomial Dirichlet; Finite mixture models; Maximum likelihood; EM; Semantic features; Image retrieval; Vistex; Cooccurrence matrix BibRef

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Liouville family of distributions; Generative models; Discriminative models; Mixture models; SVM; Bayesian inference; Exponential family; Conjugate prior; Gibbs sampling; Bayes factor; Image classification; Texture modeling BibRef

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Allili, M.S.[Mohand Saïd], Ziou, D.[Djemel], Bouguila, N.[Nizar], Boutemedjet, S.[Sabri],
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Unsupervised Feature Selection and Learning for Image Segmentation,
CRV10(285-292).
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Bouguila, N.[Nizar], El Guebaly, W.[Walid],
Discrete data clustering using finite mixture models,
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WWW Version. 0809
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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Unsupervised Feature and Model Selection for Generalized Dirichlet Mixture Models,
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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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IEEE DOI Link 0512
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Shi, Z.W.[Zhen-Wei], Tang, H.W.[Huan-Wen], Tang, Y.Y.[Yi-Yuan],
Blind source separation of more sources than mixtures using sparse mixture models,
PRL(26), No. 16, December 2005, pp. 2491-2499.
WWW Version. 0512
BibRef

Shi, Z.W.[Zhen-Wei], Zhang, C.S.[Chang-Shui],
Fast nonlinear autocorrelation algorithm for source separation,
PR(42), No. 9, September 2009, pp. 1732-1741.
Elsevier DOI Link
WWW Version. 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.
WWW Version. 0604
Image classification; Texture; Color; Gaussian mixture models; Expectation maximization; k-means; Background model; Decision fusion; Aerial images BibRef

Lin, T.I.[Tsung I.], Lee, J.C.[Jack C.], Ho, H.J.[Hsiu J.],
On fast supervised learning for normal mixture models with missing information,
PR(39), No. 6, June 2006, pp. 1177-1187.
WWW Version. Bayesian classifier; Data augmentation; EM algorithm; Incomplete features; Rao-Blackwellization 0604
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Paalanen, P.[Pekka], Kamarainen, J.K.[Joni-Kristian], Ilonen, J.[Jarmo], Kälviäinen, H.[Heikki],
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PR(39), No. 7, July 2006, pp. 1346-1358.
WWW Version. 0606
Gaussian mixture model; EM; Classifier; Confidence; Highest density region BibRef

Zhou, X., Wang, X.,
Optimisation of Gaussian mixture model for satellite image classification,
VISP(153), No. 3, June 2006, pp. 349-356.
WWW Version. 0608
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Brandt, S.S.[Sami S.],
Maximum Likelihood Robust Regression by Mixture Models,
JMIV(25), No. 1, July 2006, pp. 25-48.
Springer DOI Link 0610
BibRef

Pyun, K.[Kyungsuk], Lim, J., Won, C.S.[Chee Sun], Cray, R.M.,
Image Segmentation Using Hidden Markov Gauss Mixture Models,
IP(16), No. 7, July 2007, pp. 1902-1911.
IEEE DOI Link 0707
BibRef
Earlier: A1, A3, A2, A4:
Robust image classification based on a non-causal hidden markov gauss mixture model,
ICIP02(III: 785-788).
IEEE Abstract. 0210
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Lim, J., Pyun, K.,
Cost-Effective Hidden Markov Model-Based Image Segmentation,
SPLetters(16), No. 3, March 2009, pp. 172-175.
IEEE DOI Link 0903
BibRef

Tawfick, M.M.[Mohamad M.], Abbas, H.M.[Hazem M.], Shahein, H.I.[Hussein I.],
An integer-coded evolutionary approach for mixture maximum likelihood clustering,
PRL(29), No. 4, 1 March 2008, pp. 515-524.
WWW Version. 0711
Clustering; Mixture maximum likelihood; Evolutionary algorithms; Genetic algorithms BibRef

Goldberger, J.[Jacob], Greenspan, H.K.[Hayit K.], Dreyfuss, J.[Jeremie],
Simplifying Mixture Models Using the Unscented Transform,
PAMI(30), No. 8, August 2008, pp. 1496-1502.
IEEE DOI Link 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 Link 0706
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Reddy, C.K.[Chandan K.], Chiang, H.D.[Hsiao-Dong], Rajaratnam, B.[Bala],
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models,
PAMI(30), No. 7, July 2008, pp. 1146-1157.
IEEE DOI Link 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,
IEICE(E90-D), No. 8, August 2007, pp. 1160-1167.
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Omachi, S., Sun, F., Aso, H.,
A Discriminant Function for Noisy Pattern Recognition,
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Sun, F., Omachi, S., Aso, H.,
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.
IEEE DOI Link 0902
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IEEE DOI Link 0904
Mixed voxels. BibRef

Liu, L.F.[Li-Fan], Wang, B.[Bin], Zhang, L.M.[Li-Ming],
Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model,
PRL(30), No. 9, 1 July 2009, pp. 820-826.
Elsevier DOI Link
WWW Version. 0905
Decomposition of mixed pixels; Bayesian self-organization map (BSOM); Gaussian mixture model (GMM); Multispectral/hyperspectral data BibRef

Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.,
Image-Driven Population Analysis Through Mixture Modeling,
MedImg(28), No. 9, September 2009, pp. 1473-1487.
IEEE DOI Link 0909
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Yamada, M.[Makoto], Sugiyama, M.[Masashi],
Direct Importance Estimation with Gaussian Mixture Models,
IEICE(E92-D), No. 10, October 2009, pp. 2159-2162.
WWW Version. 0910
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Bruneau, P.[Pierrick], Gelgon, M.[Marc], Picarougne, F.[Fabien],
Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach,
PR(43), No. 3, March 2010, pp. 850-858.
Elsevier DOI Link
WWW Version. 1001
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Earlier:
Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach,
ICPR08(1-4).
IEEE DOI Link 0812
Mixture models; Bayesian estimation; Model aggregation BibRef

Filippone, M.[Maurizio], Sanguinetti, G.[Guido],
Information theoretic novelty detection,
PR(43), No. 3, March 2010, pp. 805-814.
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Novelty detection; Information theory; Mixture of Gaussians; Density estimation BibRef

Kristan, M.[Matej], Skocaj, D.[Danijel], Leonardis, A.[Ales],
Online kernel density estimation for interactive learning,
IVC(28), No. 7, July 2010, pp. 1106-1116.
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Online learning; Kernel density estimation; Mixture models; Unlearning; Compression; Hellinger distance; Unscented transform BibRef

Kristan, M.[Matej], Leonardis, A.[Ales], Skocaj, D.[Danijel],
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PR(44), No. 10-11, October-November 2011, pp. 2630-2642.
Elsevier DOI Link
WWW Version. 1101
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Earlier: A1, A2, Only:
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ICPR10(581-584).
IEEE DOI Link 1008
Online models; Probability density estimation; Kernel density estimation; Gaussian mixture models BibRef

Vogler, N.[Nadine], Bocklitz, T.[Thomas], Mariani, M.[Melissa], Deckert, V.[Volker], Markova, A.[Aneta], Schelkens, P.[Peter], Rösch, P.[Petra], Akimov, D.[Denis], Dietzek, B.[Benjamin], Popp, J.[Jürgen],
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Mei, S., He, M., Wang, Z., Feng, D.,
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Hidot, S.[Sullivan], Saint-Jean, C.[Christophe],
An Expectation-Maximization algorithm for the Wishart mixture model: Application to movement clustering,
PRL(31), No. 14, 15 October 2010, pp. 2318-2324.
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Wishart mixture model; EM algorithm; Clustering; Second-order cross moments; Movement recognition BibRef

Yang, F.[Fan], Matsushita, B.[Bunkei], Fukushima, T.[Takehiko],
A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan,
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Elsevier DOI Link
WWW Version. 1003
Impervious surface area; Spectral mixture analysis; Endmember selection; Lake Kasumigaura Basin BibRef

Rabbani, H., Gazor, S.,
Image denoising employing local mixture models in sparse domains,
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Chang, C.I., Xiong, W., Liu, W., Chang, M.L., Wu, C.C., Chen, C.C.C.,
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IEEE DOI Link 1011
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Sun, J.Y.[Jian-Yong], Kaban, A.[Ata], Garibaldi, J.M.[Jonathan M.],
Robust mixture clustering using Pearson type VII distribution,
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Ulker, Y.[Yener], Gunsel, B.[Bilge], Cemgil, A.T.[Ali Taylan],
Annealed SMC Samplers for Nonparametric Bayesian Mixture Models,
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IEEE DOI Link 1011
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Earlier:
Annealed SMC Samplers for Dirichlet Process Mixture Models,
ICPR10(2808-2811).
IEEE DOI Link 1008
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Peng, L.Q.[Liu-Qing], Zhang, J.Y.[Jun-Ying],
An entropy weighting mixture model for subspace clustering of high-dimensional data,
PRL(32), No. 8, 1 June 2011, pp. 1154-1161.
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Xie, C.H.[Cong-Hua], Song, Y.Q.[Yu-Qing], Chen, J.M.[Jian-Mei],
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Ma, Z.Y.[Zhan-Yu], Leijon, A.[Arne],
Bayesian Estimation of Beta Mixture Models with Variational Inference,
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IEEE DOI Link 1110
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Earlier:
Beta mixture models and the application to image classification,
ICIP09(2045-2048).
IEEE DOI Link 0911
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Chatzis, S.P.[Sotirios P.], Tsechpenakis, G.[Gavriil],
A possibilistic clustering approach toward generative mixture models,
PR(45), No. 5, May 2012, pp. 1819-1825.
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Possibilistic clustering; Finite mixture models BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.,
Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation,
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IEEE DOI Link 1201
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Raitoharju, M., Ali-Loytty, S.,
An Adaptive Derivative Free Method for Bayesian Posterior Approximation,
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Li, B.[Bo], Liu, W.J.[Wen-Ju], Dou, L.H.[Li-Hua],
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CDP Mixture Models for Data Clustering,
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Ari, C.[Caglar], Aksoy, S.[Selim],
Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization,
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Nielsen, F.[Frank], Boltz, S.[Sylvain], Schwander, O.[Olivier],
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Martinez-Uso, A.[Adolfo], Pla, F.[Filiberto], Sotoca, J.M.[Jose M.],
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Nacereddine, N.[Nafaa], Tabbone, S.A.[Salavatore A.], Ziou, D.[Djemel], Hamami, L.[Latifa],
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Nickisch, H.[Hannes], Rasmussen, C.E.[Carl Edward],
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models,
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Wang, R.B.[Rong-Bo], Hou, C.H.[Chao-Huan], Chen, D.[Dong],
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Levels of Details for Gaussian Mixture Models,
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Springer DOI Link 0909
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Zhao, Q.P.[Qin-Pei], Hautamaki, V.[Ville], Karkkainen, I.[Ismo], Franti, P.[Pasi],
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Mancera, L., Babacan, S.D.[S. Derin], Molina, R., Katsaggelos, A.K.,
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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.,
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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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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.],
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Reddy, C.K.[Chandan K.], Rajaratnam, B.[Bala],
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Mansjur, D.S.[Dwi Sianto], Fu, Q.A.[Qi-Ang], Juang, B.H.[Biing Hwang],
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Mansjur, D.S.[Dwi Sianto], Juang, B.H.[Biing Hwang],
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Tang, H.[Hao], Huang, T.S.[Thomas S.],
Boosting Gaussian mixture models via discriminant analysis,
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Bordes, J.B., Prinet, V.,
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Hou, S.[Shaobo], Galata, A.[Aphrodite],
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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).
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Non-parametric Estimation of Mixture Model Order,
Southwest08(145-148).
IEEE DOI Link 0803
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Santos-Villalobos, H.J.[Hector J.], Boutin, M.[Mireille],
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Palazón, V.[Vicente], Marzal, A.[Andrés], Vilar, J.M.[Juan Miguel],
Cyclic Linear Hidden Markov Models for Shape Classification,
PSIVT07(152-165).
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Palazón, V.[Vicente], Marzal, A.[Andrés],
Cyclic Viterbi Score for Linear Hidden Markov Models,
IbPRIA07(II: 339-346).
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Romero, V.[Verónica], Giménez, A.[Adrià], Juan, A.[Alfons],
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Alabau, V.[Vicente], Casacuberta, F.[Francisco], Vidal, E.[Enrique], Juan, A.[Alfons],
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Bouguila, N.[Nizar], Ziou, D.[Djemel], Hammoud, R.I.[Riad I.],
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Luo, B.[Bin], Chen, S.B.[Si-Bao],
LPP and LPP Mixtures for Graph Spectral Clustering,
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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).
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EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models,
ICPR06(II: 451-455).
WWW Version. 0609
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Lin, B.[Bin], Wang, X.J.[Xian-Ji], Zhong, R.T.[Run-Tian], Zhuang, Z.Q.[Zhen-Quan],
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Ilonen, J., Paalanen, P., Kamarainen, J.K., Kalviainen, H.,
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ICPR06(II: 577-580).
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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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Abd-Almageed, W.[Wael], Davis, L.S.[Larry S.],
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Zhu, Y.N.[Ya-Nong], Fisher, M.H.[Mark H.], Zwiggelaar, R.[Reyer],
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Ali, A.M.[Asem M.], Farag, A.A.[Amal A.], Farag, A.A.[Aly A.],
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Farag, A.A., El-Baz, A., Gimel'farb, G.,
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ICIP04(III: 1871-1874).
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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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Franc, V.[Vojtech], Hlavác, V.[Václav],
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ICPR00(Vol II: 29-33).
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Hammoud, R., Mohr, R.,
Mixture Densities for Video Objects Recognition,
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Zwart, J.P., Kröse, B.,
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Somol, P., Grim, J.[Jiri], Novovicova, J.[Jana], Pudil, P.[Pavel], Ferri, F.J.[Francesc J.],
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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],
Bimodal histogram transformation based on maximum likelihood parameter estimates in univariate Gaussian mixtures,
CIAP97(II: 532-543).
WWW Version. 9709
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixed Pixels, Unmixing .