14.2.6 Mixture Models, Mixed Pixels

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Lawoko, C.R.O., McLachlan, G.J.,
Further results on discrimination with autocorrelated observations,
PR(21), No. 1, 1988, pp. 69-72.
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McLachlan, G.[Geoff], Peel, D.,
Mixfit: An Algorithm for the Automatic Fitting and Testing of Normal Mixture Models,
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The relative neighborhood graph for mixed feature variables,
PR(18), No. 2, 1985, pp. 161-167.
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Yarman-Vural, F.[Fatos], Ataman, E.[Ergin],
Noise, histogram and cluster validity for Gaussian-mixtured data,
PR(20), No. 4, 1987, pp. 385-401.
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Derin, H.,
Estimating Components of Univariate Gaussian Mixtures Using Prony's Method,
PAMI(9), No. 1, January 1987, pp. 142-148. BibRef 8701

Parker, I.M.,
Finding the overlap integral between two classes of object,
PR(26), No. 7, July 1993, pp. 1117-1119.
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Santago, P., Gage, H.D.,
Statistical models of partial volume effect,
IP(4), No. 11, November 1995, pp. 1531-1540.
WWW Version. 0402 BibRef

Zhuang, X.H., Huang, Y., Palaniappan, K., Zhao, Y.X.,
Gaussian Mixture Density Modeling, Decomposition, and Applications,
IP(5), No. 9, September 1996, pp. 1293-1302.
WWW Version. Extraction of clusters. BibRef 9609

Schowengerdt, R.A.,
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PRL(17), No. 13, November 25 1996, pp. 1379-1387. 9701 BibRef

Delignon, Y., Marzouki, A., Pieczynski, W.,
Estimation of Generalized Mixtures and Its Application in Image Segmentation,
IP(6), No. 10, October 1997, pp. 1364-1375.
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Fjørtoft, R.[Roger], Pieczynski, W.[Wojciech], Delignon, Y.[Yves],
Generalised Mixture Estimation and Unsupervised Classification based on Hidden Markov Chains and Hidden Markov Random Fields,
SCIA01(O-Th3A). 0206 BibRef

Pieczynski, W., Bouvrais, J., Michel, C.,
Estimation of Generalized Mixture in the Case of Correlated Sensors,
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Sand, F., Dougherty, E.R.,
Asymptotic Granulometric Mixing Theorem: Morphological Estimation of Sizing Parameters and Mixture Proportions,
PR(31), No. 1, January 1998, pp. 53-61.
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Kehtarnavaz, N.[Nasser], Nakamura, E.[Eiji],
Generalization of the EM Algorithm for Mixture Density Estimation,
PRL(19), No. 2, February 1998, pp. 133-140. 9808 BibRef

Medasani, S.[Swarup], Krishnapuram, R.[Raghu],
A Comparison of Gaussian and Pearson Mixture Modeling for Pattern Recognition and Computer Vision Applications,
PRL(20), No. 3, March 1999, pp. 305-313. BibRef 9903

Celeux, G.[Gilles], Govaert, G.[Gérard],
Gaussian parsimonious clustering models,
PR(28), No. 5, May 1995, pp. 781-793.
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Biernacki, C.[Christophe], Celeux, G.[Gilles], Govaert, G.[Gérard],
An improvement of the NEC criterion for assessing the number of clusters in a mixture model,
PRL(20), No. 3, March 1999, pp. 267-272. BibRef 9903

Biernacki, C.[Christophe], Celeux, G.[Gilles], Govaert, G.[Gerard],
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood,
PAMI(22), No. 7, July 2000, pp. 719-725.
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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:
A stochastic model of mixels and image classification,
ICPR96(II: 745-749).
WWW Version. 9608(Univ. of Tokyo, J) BibRef

Cootes, T.F., Taylor, C.J.,
A Mixture Model for Representing Shape Variation,
IVC(17), No. 8, June 1999, pp. 567-573.
WWW Version. BibRef 9906
Earlier: BMVC97(xx-yy).
HTML Version. 0209 BibRef

Boshra, M.[Michael], Zhang, H.[Hong],
Accommodating uncertainty in pixel-based verification of 3-D object hypotheses,
PRL(20), No. 7, July 1999, pp. 689-698. BibRef 9907

Aguiar, A.P.D., Shimabukuro, Y.E., Mascarenhas, N.D.A.,
Use of synthetic bands derived from mixing models in the multispectral classification of remote sensing images,
JRS(20), No. 4, March 1999, pp. 647. BibRef 9903

Tadjudin, S., Landgrebe, D.A.,
Robust Parameter Estimation for Mixture Model,
GeoRS(38), No. 1, January 2000, pp. 439-445.
IEEE Top Reference. 0002 BibRef

Erol, H.[Hamza],
A practical method for constructing the mixture model for a spectral class,
JRS(21), No. 4, March 2000, pp. 823. 0002 BibRef

Geva, A.B.[Amir B.], Steinberg, Y.[Yossef], Bruckmair, S.[Shay], Nahum, G.[Gerry],
A comparison of cluster validity criteria for a mixture of normal distributed data,
PRL(21), No. 6-7, June 2000, pp. 511-529. 0006 BibRef

Martínez, A.M.[Aleix M.], Vitrià, J.[Jordi],
Learning mixture models using a genetic version of the EM algorithm,
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Brown, M., Lewis, H.G., Gunn, S.R.,
Linear Spectral Mixture Models and Support Vector Machines for Remote Sensing,
GeoRS(38), No. 5, September 2000, pp. 2346-2360.
IEEE Top Reference. 0010 BibRef

Lee, T.W.[Te-Won], Lewicki, M.S.[Michael S.], Sejnowski, T.J.[Terrence J.],
ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Separation,
PAMI(22), No. 10, October 2000, pp. 1078-1089.
IEEE Abstract. IEEE Top Reference.
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Lee, T.W.[Te-Won], Lewicki, M.S.,
Unsupervised image classification, segmentation, and enhancement using ICA mixture models,
IP(11), No. 3, March 2002, pp. 270-279.
WWW Version. 0203 BibRef

Park, H.J.[Hyun-Jin], Lee, T.W.[Te-Won],
Unsupervised learning of nonlinear dependencies in natural images,
IJIST(15), No. 1, 2005, pp. 34-47.
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Carreira-Perpiñán, M.Á.[Miguel Á.],
Mode-Finding for Mixtures of Gaussian Distributions,
PAMI(22), No. 11, November 2000, pp. 1318-1323.
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WWW Version. 0012Matlab implementation and TR with mathematical details:
HTML Version. Code, Modes. BibRef

Carreira-Perpiñán, M.Á.[Miguel Á.], Williams, C.K.I.[Christopher K.I.],
On the Number of Modes of a Gaussian Mixture,
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Carreira-Perpinan, M.A.[Miguel A.],
Gaussian Mean-Shift Is an EM Algorithm,
PAMI(29), No. 5, May 2007, pp. 767-776.
WWW Version. 0704 BibRef
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Acceleration Strategies for Gaussian Mean-Shift Image Segmentation,
CVPR06(I: 1160-1167).
WWW Version. 0606 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

Heinz, D.C., Chang, C.I.,
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery,
GeoRS(39), No. 3, March 2001, pp. 529-545.
IEEE Top Reference. 0104 See also Anomaly detection and classification for hyperspectral imagery. BibRef

Chang, C.I., Ji, B.,
Weighted Abundance-Constrained Linear Spectral Mixture Analysis,
GeoRS(44), No. 2, February 2006, pp. 378-388.
WWW Version. 0602 BibRef

Chang, C.I., Ji, B.,
Fisher's Linear Spectral Mixture Analysis,
GeoRS(44), No. 8, August 2006, pp. 2292-2304.
WWW Version. 0608 BibRef

Chang, C.I.[Chein-I], Chiang, S.S.[Shao-Shan], Smith, J.A., Ginsberg, I.W.,
Linear spectral random mixture analysis for hyperspectral imagery,
GeoRS(40), No. 2, February 2002, pp. 375-392.
IEEE Top Reference. 0205 BibRef

Yang, Z.R.[Zheng Rong], Zwolinski, M.[Mark],
Mutual Information Theory for Adaptive Mixture Models,
PAMI(23), No. 4, April 2001, pp. 396-403.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0104Analyze 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,
SP(81), No. 4, April 2001, pp. 883-887.
HTML Version. 0105 BibRef

Dahmen, J.[Jörg], Keysers, D.[Daniel], Ney, H.[Hermann], Güld, M.O.[Mark Oliver],
Statistical Image Object Recognition using Mixture Densities,
JMIV(14), No. 3, May 2001, pp. 285-296.
WWW Version. 0106 BibRef
Earlier: A1, A2, A4, A3:
Invariant Image Object Recognition Using Mixture Densities,
ICPR00(Vol II: 614-617).
WWW Version.
HTML Version. 0009 BibRef

Keysers, D.[Daniel], Macherey, W.[Wolfgang], Ney, H.[Hermann], Dahmen, J.[Jorg],
Adaptation in Statistical Pattern Recognition Using Tangent Vectors,
PAMI(26), No. 2, February 2004, pp. 269-274.
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Rand, R.S., Keenan, D.M.,
A spectral mixture process conditioned by Gibbs-based partitioning,
GeoRS(39), No. 7, July 2001, pp. 1421-1434.
IEEE Top Reference. 0108 BibRef

Collins, E.F., Roberts, D.A., Borel, C.C.,
Spectral mixture analysis of simulated thermal infrared spectrometry data: an initial temperature estimate bounded TESSMA search approach,
GeoRS(39), No. 7, July 2001, pp. 1435-1446.
IEEE Top Reference. 0108 BibRef

Yang, X.[Xiangyu], Liu, J.[Jun],
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PRL(23), No. 5, March 2002, pp. 501-512.
HTML Version. 0202 BibRef

Figueiredo, M.A.T., Jain, A.K.,
Unsupervised Learning of Finite Mixture Models,
PAMI(24), No. 3, March 2002, pp. 381-396.
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WWW Version. 0202 BibRef
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Unsupervised Selection and Estimation of Finite Mixture Models,
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Figueiredo, M.A.T.,
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Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.,
Simultaneous Feature Selection and Clustering Using Mixture Models,
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Dattatreya, G.R.,
Unsupervised context estimation in a mesh of pattern classes for image recognition,
PR(24), No. 7, 1991, pp. 685-694.
WWW Version. 0401 BibRef

Dattatreya, G.R.,
Gaussian mixture parameter estimation with known means and unknown class-dependent variances,
PR(35), No. 7, July 2002, pp. 1611-1616.
WWW Version. 0204 BibRef

Dattatreya, G.R.,
Estimation of class correlation parameters in images for context classification,
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Dattatreya, G.R., Fang, X.F.[Xiaori Frank],
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Aylward, S.R.[Stephen R.],
Continuous mixture modeling via goodness-of-fit ridges,
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Govaert, G.[Gérard], Nadif, M.[Mohamed],
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Asner, G.P., Heidebrecht, K.B.,
Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations,
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Frey, B.J.[Brendan J.], Jojic, N.[Nebojsa],
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Estimating Mixture Models of Images and Inferring Spatial Transformations Using the EM Algorithm,
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Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components,
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Frey, B.J., Jojic, N., Kannan, A.,
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Constantinopoulos, C.[Constantinos], Titsias, M.K., Likas, A.C.[Aristidis C.],
Bayesian Feature and Model Selection for Gaussian Mixture Models,
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WWW Version. 0605Feature and model selection together BibRef

Nikou, C.[Christophoros], Galatsanos, N.P.[Nikolaos P.], Likas, A.C.[Aristidis C.],
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Constantinopoulos, C.[Constantinos], Likas, A.C.[Aristidis C.],
Image Modeling and Segmentation Using Incremental Bayesian Mixture Models,
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Zhang, Z.H.[Zhi-Hua], Chen, C.B.[Chi-Biao], Sun, J.[Jian], Chan, K.L.[Kap Luk],
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Igual, J.[Jorge], Camacho, A.[Andres], Bernabeu, P.[Pablo], Vergara, L.[Luis],
A maximum a posteriori estimate for the source separation problem with statistical knowledge about the mixing matrix,
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Rashed, T.[Tarek], Weeks, J.R.[John R.], Roberts, D.[Dar], Rogan, J.[John], Powell, R.[Rebecca],
Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models,
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WWW Version. 0309The results showed that a majority of the image could be modeled successfully with two- or three-endmember models. BibRef

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Zivkovic, Z.[Zoran], van der Heijden, F.[Ferdinand],
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Settle, J.J.,
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Xiong, Y.M.[Yi-Min], Yeung, D.Y.[Dit-Yan],
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Bouguila, N., Ziou, D., Vaillancourt, J.,
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Bouguila, N.[Nizar], Ziou, D.[Djemel],
Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications,
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WWW Version. 0508 BibRef
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Bouguila, N.[Nizar], Ziou, D.[Djemel],
A Hybrid SEM Algorithm for High-Dimensional Unsupervised Learning Using a Finite Generalized Dirichlet Mixture,
IP(15), No. 9, August 2006, pp. 2657-2668.
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Powerful finite mixture model based on the generalized dirichlet distribution: unsupervised learning and applications,
ICPR04(I: 280-283).
WWW Version. 0409 BibRef

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Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization,
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WWW Version. 0711Multinomial Dirichlet; Finite mixture models; Maximum likelihood; EM; Semantic features; Image retrieval; Vistex; Cooccurrence matrix BibRef

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Jin, H.D.[Hui-Dong], Leung, K.S.[Kwong-Sak], Wong, M.L.[Man-Leung], Xu, Z.B.[Zong-Ben],
Scalable model-based cluster analysis using clustering features,
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Jin, H.D.[Hui-Dong], Wong, M.L.[Man-Leung], Leung, K.S.,
Scalable Model-Based Clustering for Large Databases Based on Data Summarization,
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WWW Version. 0510 BibRef

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],
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models,
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Zhang, B.B.[Bai-Bo], Zhang, C.S.[Chang-Shui], Yi, X.[Xing],
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Haertel, V.F., Shimabukuro, Y.E.,
Spectral linear mixing model in low spatial resolution image data,
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WWW Version. 0512 BibRef

Shi, Z.[Zhenwei], Tang, H.[Huanwen], Tang, Y.[Yiyuan],
Blind source separation of more sources than mixtures using sparse mixture models,
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WWW Version. 0512 BibRef

Kwan, C., Ayhan, B., Chen, G., Wang, J., Ji, B., Chang, C.I.,
A Novel Approach for Spectral Unmixing, Classification, and Concentration Estimation of Chemical and Biological Agents,
GeoRS(44), No. 2, February 2006, pp. 409-419.
WWW Version. 0602 BibRef

Permuter, H.[Haim], Francos, J.[Joseph], 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. 0604Image 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.],
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WWW Version. Bayesian classifier; Data augmentation; EM algorithm; Incomplete features; Rao-Blackwellization 0604 BibRef

Paalanen, P.[Pekka], Kamarainen, J.K.[Joni-Kristian], Ilonen, J.[Jarmo], Kälviäinen, H.[Heikki],
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Zhou, X., Wang, X.,
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WWW Version. 0608 BibRef

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Foody, G.M.[Giles M.], Doan, H.T.X.,
Variability in Soft Classification Prediction and Its Implications for Sub-pixel Scale Change Detection and Super Resolution Mapping,
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WWW Version. 0711 BibRef
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Improved Stone's Complexity Pursuit for Hyperspectral Imagery Unmixing,
ICPR06(IV: 817-820).
WWW Version. 0609 BibRef
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WWW Version. 0608 BibRef

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Boutin, M.[Mireille], Comer, M.[Mary],
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García, V., Mollineda, R.A., Sánchez, J.S., Alejo, R., Sotoca, J.M.,
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Palazón, V.[Vicente], Marzal, A.[Andrés],
Cyclic Viterbi Score for Linear Hidden Markov Models,
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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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Wang, P.[Peng], Kohler, C.[Christian], Verma, R.[Ragini],
Estimating Cluster Overlap on Manifolds and its Application to Neuropsychiatric Disorders,
ComponentAnalysis07(1-6).
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Luo, B.[Bin], Chen, S.[Sibao],
LPP and LPP Mixtures for Graph Spectral Clustering,
PSIVT06(118-127).
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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).
WWW Version. 0608 BibRef
Earlier:
EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models,
ICPR06(II: 451-455).
WWW Version. 0609 BibRef

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).
WWW Version. 0609 BibRef

Ilonen, J., Paalanen, P., Kamarainen, J.K., Kalviainen, H.,
Gaussian mixture pdf in one-class classification: computing and utilizing confidence values,
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).
WWW Version. 0609 BibRef

Chen, D.T.[Da-Tong], Yang, J.[Jie],
Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models,
ICPR06(II: 1078-1081).
WWW Version. 0609 BibRef

Abd-Almageed, W.[Wael], Davis, L.S.[Larry S.],
Density Estimation Using Mixtures of Mixtures of Gaussians,
ECCV06(IV: 410-422).
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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).
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Farag, A.A., El-Baz, A., Gimel'farb, G.,
Density estimation using modified expectation-maximization algorithm for a linear combination of gaussians,
ICIP04(III: 1871-1874).
WWW Version. 0505 BibRef
And:
Expectation-maximization for a linear combination of Gaussians,
ICPR04(III: 422-425).
WWW Version. 0409 BibRef

de Ridder, D., Franc, V.,
Robust subspace mixture models using t-distributions,
BMVC03(xx-yy).
HTML Version. 0409 BibRef

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).
WWW Version. 0409 BibRef

Sminchisescu, C.[Cristian], Jepson, A.[Allan],
Variational mixture smoothing for non-linear dynamical systems,
CVPR04(II: 608-615).
IEEE Abstract. IEEE Top Reference. 0408 BibRef

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).
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Vermaak, J., Doucet, A., Perez, P.,
Maintaining multi-modality through mixture tracking,
ICCV03(1110-1116).
WWW Version. 0311 BibRef

Pyun, K.[Kyungsuk], Won, C.S.[Chee Sun], Lim, J., Cray, R.M.,
Robust image classification based on a non-causal hidden markov gauss mixture model,
ICIP02(III: 785-788).
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Franc, V.[Vojtech], Hlavác, V.[Václav],
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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 BibRef

Xuan, G., Zhang, W., Chai, P.,
EM Algorithms of Gaussian Mixture Model and Hidden Markov Model,
ICIP01(I: 145-148).
IEEE Abstract. IEEE Top Reference. 0108 BibRef

Wilson, R., Calway, A.D.,
Multiresolution Gaussian Mixture Models for Visual Motion Estimation,
ICIP01(II: 921-924).
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Wilson, R.[Ronald],
MGMM: Multiresolution Gaussian Mixture Models for Computer Vision,
ICPR00(Vol I: 212-215).
WWW Version.
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Kudo, M., Imai, H., Shimbo, M.,
A Histogram-based Classifier on Overlapped Bins,
ICPR00(Vol II: 29-33).
WWW Version.
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Hammoud, R., Mohr, R.,
Mixture Densities for Video Objects Recognition,
ICPR00(Vol II: 71-75).
WWW Version.
HTML Version. 0009 BibRef

Zwart, J.P., Kröse, B.,
Constrained Mixture Modeling of Intrinsically Low-dimensional Distributions,
ICPR00(Vol II: 610-613).
WWW Version.
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Omachi, S., Sun, F., Aso, H.,
A Discriminant Function for Noisy Pattern Recognition,
SCIA99(Statistical Methods). BibRef 9900

Sun, F., Omachi, S., Aso, H.,
An Algorithm for Estimating Mixture Distribution of High Dimensional Vectors and its Application to Character Recognition,
SCIA99(Statistical Methods). BibRef 9900

Somol, P., Grim, J.[Jiri], Novovicova, J.[Jana], Pudil, P.[Pavel], Ferri, F.[Francesc],
Initializing Normal Mixtures of Densities,
ICPR98(Vol I: 886-890).
WWW Version. 9808 BibRef

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).
WWW Version. 9808 BibRef

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 BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixed Pixels, Subpixel Classification .