14.2.6 Mixture Models, Mixed Pixels

Chapter Contents (Back)
Mixture Models. 9905

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WWW Version. Extraction of clusters. BibRef 9609

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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],
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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.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0008 BibRef

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).
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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,
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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],
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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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PAMI(22), No. 10, October 2000, pp. 1078-1089.
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Lee, T.W.[Te-Won], Lewicki, M.S.,
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IP(11), No. 3, March 2002, pp. 270-279.
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Park, H.J.[Hyun-Jin], Lee, T.W.[Te-Won],
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IJIST(15), No. 1, 2005, pp. 34-47.
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Carreira-Perpiñán, M.Á.[Miguel Á.],
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PAMI(22), No. 11, November 2000, pp. 1318-1323.
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WWW Version. 0012Matlab implementation and TR with mathematical details:
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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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PAMI(29), No. 5, May 2007, pp. 767-776.
WWW Version. 0704 BibRef
Earlier:
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.,
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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.
IEEE Abstract. IEEE Top Reference. 0402Integrate the tangent method into statistical framework to improve classisification. BibRef

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.,
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PAMI(24), No. 3, March 2002, pp. 381-396.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0202 BibRef
Earlier:
Unsupervised Selection and Estimation of Finite Mixture Models,
ICPR00(Vol II: 87-90).
WWW Version.
HTML Version. 0009 BibRef

Figueiredo, M.A.T.,
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Simultaneous Feature Selection and Clustering Using Mixture Models,
PAMI(26), No. 9, September 2004, pp. 1154-1166.
IEEE Abstract. IEEE Top Reference. 0409Add 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.,
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.
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Dattatreya, G.R.,
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Dattatreya, G.R., Fang, X.F.[Xiaori Frank],
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Aylward, S.R.[Stephen R.],
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Govaert, G.[Gérard], Nadif, M.[Mohamed],
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PR(36), No. 2, February 2003, pp. 463-473.
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Govaert, G.[Gerard], Nadif, M.[Mohamed],
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Asner, G.P., Heidebrecht, K.B.,
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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,
CVPR99(I: 416-422).
IEEE Abstract. IEEE Top Reference.
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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.,
Learning appearance and transparency manifolds of occluded objects in layers,
CVPR03(I: 45-52).
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Jojic, N.[Nebojsa], Petrovic, N.[Nemanja], Frey, B.J.[Brendan J.], Huang, T.S.[Thomas S.],
Transformed Hidden Markov Models: Estimating Mixture Models of Images and Inferring Spatial Transformations in Video Sequences,
CVPR00(II: 26-33).
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WWW Version. 0005 BibRef

Titsias, M.K., Likas, A.C.,
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PAMI(25), No. 7, July 2003, pp. 924-928.
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Constantinopoulos, C.[Constantinos], Titsias, M.K., Likas, A.C.[Aristidis C.],
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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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IP(16), No. 4, April 2007, pp. 1121-1130.
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Constantinopoulos, C.[Constantinos], Likas, A.C.[Aristidis C.],
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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,
PRL(24), No. 15, November 2003, pp. 2519-2523.
WWW Version. 0308 BibRef

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,
PhEngRS(69), No. 9, September 2003, pp. 1011-1020.
WWW Version. 0309The results showed that a majority of the image could be modeled successfully with two- or three-endmember models. BibRef

Zhang, B.B.[Bai-Bo], Zhang, C.S.[Chang-Shui], Yi, X.[Xing],
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Zhang, M.H.[Ming-Heng], Cheng, Q.S.[Qian-Sheng],
Determine the number of components in a mixture model by the extended KS test,
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Liu, W.X.[Wei-Xiang], Zheng, N.N.[Nan-Ning],
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Zivkovic, Z.[Zoran], van der Heijden, F.[Ferdinand],
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Wang, H.X.[Hai Xian], Zhang, Q.B.[Quan Bing], Luo, B.[Bin], Wei, S.[Sui],
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WWW Version. 0405 BibRef

Wang, H.X.[Hai Xian], Luo, B.[Bin], Zhang, Q.B.[Quan Bing], Wei, S.[Sui],
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Settle, J.J.,
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Settle, J.J.,
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Settle, J.,
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GeoRS(44), No. 2, February 2006, pp. 389-396.
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Xiong, Y.M.[Yi-Min], Yeung, D.Y.[Dit-Yan],
Time series clustering with ARMA mixtures,
PR(37), No. 8, August 2004, pp. 1675-1689.
WWW Version. 0407 BibRef

Yang, X.[Xiangyu], Krishnan, S.M.[Shankar M.],
Image segmentation using finite mixtures and spatial information,
IVC(22), No. 9, 20 August 2004, pp. 735-745.
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Bouguila, N., Ziou, D., Vaillancourt, J.,
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IP(13), No. 11, November 2004, pp. 1533-1543.
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Bouguila, N.[Nizar], Ziou, D.[Djemel],
Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications,
PRL(26), No. 12, September 2005, pp. 1916-1925.
WWW Version. 0508 BibRef
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MML-Based Approach for High-Dimensional Unsupervised Learning Using the Generalized Dirichlet Mixture,
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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.
WWW Version. 0608 BibRef
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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

Bouguila, N.[Nizar], Ziou, D.[Djemel],
Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization,
JVCIR(18), No. 4, August 2007, pp. 295-309.
WWW Version. 0711Multinomial Dirichlet; Finite mixture models; Maximum likelihood; EM; Semantic features; Image retrieval; Vistex; Cooccurrence matrix BibRef

Bouguila, N.[Nizar], Ziou, D.[Djemel],
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length,
PAMI(29), No. 10, October 2007, pp. 1716-1731.
WWW Version. 0710Structure of data withou knowing number of clusters. 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],
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Jin, H.D.[Hui-Dong], Wong, M.L.[Man-Leung], Leung, K.S.,
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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.],
Lloyd clustering of Gauss mixture models for image compression and classification,
SP:IC(20), No. 5, June 2005, pp. 459-485.
WWW Version. 0506 BibRef

Pernkopf, F.[Franz], Bouchaffra, D.[Djamel],
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models,
PAMI(27), No. 8, August 2005, pp. 1344-1348.
IEEE Abstract. IEEE Top Reference. 0506 BibRef

Zhang, B.B.[Bai-Bo], Zhang, C.S.[Chang-Shui], Yi, X.[Xing],
Active curve axis Gaussian mixture models,
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WWW Version. 0510 BibRef

Ma, J.[Jinwen], Fu, S.Q.[Shu-Qun],
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WWW Version. 0510 BibRef

Haertel, V.F., Shimabukuro, Y.E.,
Spectral linear mixing model in low spatial resolution image data,
GeoRS(43), No. 11, November 2005, pp. 2555-2562.
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,
PRL(26), No. 16, December 2005, pp. 2491-2499.
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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PR(39), No. 6, June 2006, pp. 1177-1187.
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],
Feature representation and discrimination based on Gaussian mixture model probability densities: Practices and algorithms,
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WWW Version. 0606Gaussian 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 BibRef

Brandt, S.S.[Sami S.],
Maximum Likelihood Robust Regression by Mixture Models,
JMIV(25), No. 1, July 2006, pp. 25-48.
WWW Version. 0610 BibRef

Rogge, D.M., Rivard, B., Zhang, J., Feng, J.,
Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets,
GeoRS(44), No. 12, December 2006, pp. 3725-3736.
WWW Version. 0701 BibRef

Pyun, K., Lim, J., Won, C.S., Gray, R.M.,
Image Segmentation Using Hidden Markov Gauss Mixture Models,
IP(16), No. 7, July 2007, pp. 1902-1911.
WWW Version. 0707 BibRef

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,
PhEngRS(73), No. 8, August 2007, pp. 923-934.
WWW Version. 0709The impacts of class spectral variability on unmixing the the implications for analyses based on soft classification outputs. BibRef

Jia, S.[Sen], Qian, Y.T.[Yun-Tao],
Spectral and Spatial Complexity-Based Hyperspectral Unmixing,
GeoRS(45), No. 12, December 2007, pp. 3867-3879.
WWW Version. 0711 BibRef
Earlier:
Improved Stone's Complexity Pursuit for Hyperspectral Imagery Unmixing,
ICPR06(IV: 817-820).
WWW Version. 0609 BibRef
And:
MRF Based Spatial Complexity for Hyperspectral Imagery Unmixing,
SSPR06(531-540).
WWW Version. 0608 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,
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Goldberger, J.[Jacob], Greenspan, H.K.[Hayit K.], Dreyfuss, J.[Jeremie],
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Miao, L.[Lidan], Qi, H.R.[Hai-Rong],
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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],
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Romero, V.[Verónica], Giménez, A.[Adrià], Juan, A.[Alfons],
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IbPRIA07(I: 539-546).
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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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Wang, P.[Peng], Kohler, C.[Christian], Verma, R.[Ragini],
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Luo, B.[Bin], Chen, S.[Sibao],
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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,
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Ilonen, J., Paalanen, P., Kamarainen, J.K., Kalviainen, H.,
Gaussian mixture pdf in one-class classification: computing and utilizing confidence values,
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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,
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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).
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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).
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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,
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Sminchisescu, C.[Cristian], Jepson, A.[Allan],
Variational mixture smoothing for non-linear dynamical systems,
CVPR04(II: 608-615).
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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,
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Vermaak, J., Doucet, A., Perez, P.,
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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,
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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).
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Wilson, R., Calway, A.D.,
Multiresolution Gaussian Mixture Models for Visual Motion Estimation,
ICIP01(II: 921-924).
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MGMM: Multiresolution Gaussian Mixture Models for Computer Vision,
ICPR00(Vol I: 212-215).
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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.
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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 .


Last update:Jun 25, 2008 at 13:37:57