14.2.8 Mixture Models, Mixed Pixels

Bouguila, N.[Nizar],
Bayesian hybrid generative discriminative learning based on finite Liouville mixture models,
PR(44), No. 6, June 2011, pp. 1183-1200.
Elsevier DOI 1102
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

Bouguila, N.[Nizar],
Infinite Liouville mixture models with application to text and texture categorization,
PRL(33), No. 2, 15 January 2012, pp. 103-110.
Elsevier DOI 1112
Liouville family of distributions; Infinite mixture models; Proportional data; Nonparametric Bayesian inference; MCMC; Gibbs sampling BibRef

Allili, M.S.[Mohand Saïd], Ziou, D.[Djemel], Bouguila, N.[Nizar], Boutemedjet, S.[Sabri],
Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection,
CirSysVideo(20), No. 10, October 2010, pp. 1373-1377.
IEEE DOI 1011
BibRef
Earlier:
Unsupervised Feature Selection and Learning for Image Segmentation,
CRV10(285-292).
IEEE DOI 1005
BibRef

Larivière, G.[Guillaume], Allili, M.S.[Mohand Saïd],
A Learning Probabilistic Approach for Object Segmentation,
CRV12(86-93).
IEEE DOI 1207
BibRef

Bouguila, N.[Nizar], El Guebaly, W.[Walid],
Discrete data clustering using finite mixture models,
PR(42), No. 1, January 2009, pp. 33-42.
Elsevier DOI 0809
Discrete data; Finite mixture models; Multinomial; Generalized Dirichlet distribution; EM; Spatial color; Image databases; Labeled and unlabeled images; Summarization; Text classification BibRef

Maanicshah, K.[Kamal], Amayri, M.[Manar], Bouguila, N.[Nizar],
Interactive Generalized Dirichlet Mixture Allocation Model,
SSSPR22(33-42).
Springer DOI 2301
BibRef

Nguyen, H.[Hieu], Maanicshah, K.[Kamal], Azam, M.[Muhammad], Bouguila, N.[Nizar],
Data Clustering Using Variational Learning of Finite Scaled Dirichlet Mixture Models with Component Splitting,
ICIAR19(II:117-128).
Springer DOI 1909
BibRef

Maanicshah, K.[Kamal], Ali, S.[Samr], Fan, W.T.[Wen-Tao], Bouguila, N.[Nizar],
Unsupervised Variational Learning of Finite Generalized Inverted Dirichlet Mixture Models with Feature Selection and Component Splitting,
ICIAR19(II:94-105).
Springer DOI 1909
BibRef

Bouguila, N.[Nizar],
Non-Gaussian mixture image models prediction,
ICIP08(2580-2583).
IEEE DOI 0810
BibRef

Boutemedjet, S.[Sabri], Bouguila, N.[Nizar], Ziou, D.[Djemel],
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering,
PAMI(31), No. 8, August 2009, pp. 1429-1443.
IEEE DOI 0906
BibRef
Earlier:
Unsupervised Feature and Model Selection for Generalized Dirichlet Mixture Models,
ICIAR07(330-341).
Springer DOI 0708
Feature selection in mixtures BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
Generalized Gaussian mixture models as a nonparametric Bayesian approach for clustering using class-specific visual features,
JVCIR(23), No. 8, November 2012, pp. 1199-1212.
Elsevier DOI 1211
Mixture models; Generalized Gaussian; Feature selection; Nonparametric Bayes; MCMC; Gibbs sampling; Photographic; Painting; Segmentation; Infrared images BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
Finite asymmetric generalized Gaussian mixture models learning for infrared object detection,
CVIU(117), No. 12, 2013, pp. 1659-1671.
Elsevier DOI 1310
Infrared
See also Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection. BibRef

Song, Z.Y.[Zi-Yang], Ali, S.[Samr], Bouguila, N.[Nizar],
Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection,
IET-IPR(14), No. 11, September 2020, pp. 2321-2332.
DOI Link 2009
BibRef
Earlier:
Bayesian Learning of Infinite Asymmetric Gaussian Mixture Models for Background Subtraction,
ICIAR19(I:264-274).
Springer DOI 1909
BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
Simultaneous high-dimensional clustering and feature selection using asymmetric Gaussian mixture models,
IVC(34), No. 1, 2015, pp. 27-41.
Elsevier DOI 1502
Asymmetric Gaussian distribution BibRef

Bouguila, N.[Nizar], Ghimire, M.N.[Mukti Nath],
Discrete visual features modeling via leave-one-out likelihood estimation and applications,
JVCIR(21), No. 7, October 2010, pp. 613-626.
Elsevier DOI 1003
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],
Scalable model-based cluster analysis using clustering features,
PR(38), No. 5, May 2005, pp. 637-649.
Elsevier DOI 0501
BibRef

Jin, H.D.[Hui-Dong], Wong, M.L.[Man-Leung], Leung, K.S.,
Scalable Model-Based Clustering for Large Databases Based on Data Summarization,
PAMI(27), No. 11, November 2005, pp. 1710-1719.
IEEE DOI 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.],
Lloyd clustering of Gauss mixture models for image compression and classification,
SP:IC(20), No. 5, June 2005, pp. 459-485.
Elsevier DOI 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. 0506
BibRef

Zhang, B.B.[Bai-Bo], Zhang, C.S.[Chang-Shui], Yi, X.[Xing],
Active curve axis Gaussian mixture models,
PR(38), No. 12, December 2005, pp. 2351-2362.
Elsevier DOI 0510
BibRef

Ma, J.W.[Jin-Wen], Fu, S.Q.[Shu-Qun],
On the correct convergence of the EM algorithm for Gaussian mixtures,
PR(38), No. 12, December 2005, pp. 2602-2611.
Elsevier DOI 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.
IEEE DOI 0512
BibRef

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.
Elsevier DOI 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 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.
Elsevier DOI 0604
Image classification; Texture; Color; Gaussian mixture models; Expectation maximization; k-means; Background model; Decision fusion; Aerial images 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,
PR(39), No. 7, July 2006, pp. 1346-1358.
Elsevier DOI 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.
DOI Link 0608
BibRef

Brandt, S.S.[Sami S.],
Maximum Likelihood Robust Regression by Mixture Models,
JMIV(25), No. 1, July 2006, pp. 25-48.
Springer DOI 0610
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.
Elsevier DOI 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 0806
BibRef

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
BibRef

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 0806
BibRef

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.
DOI Link 0708
BibRef

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

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 0902
BibRef
And: Erratum: MedImg(28), No. 4, April 2009, pp. 631-631.
IEEE DOI 0904
Mixed voxels. 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 0909
BibRef

Yamada, M.[Makoto], Sugiyama, M.[Masashi],
Direct Importance Estimation with Gaussian Mixture Models,
IEICE(E92-D), No. 10, October 2009, pp. 2159-2162.
WWW Link. 0910
BibRef

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 1001
BibRef
Earlier:
Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach,
ICPR08(1-4).
IEEE DOI 0812
Mixture models; Bayesian estimation; Model aggregation 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.
Elsevier DOI 1006
Online learning; Kernel density estimation; Mixture models; Unlearning; Compression; Hellinger distance; Unscented transform BibRef

Kristan, M.[Matej], Leonardis, A.[Ales], Skocaj, D.[Danijel],
Multivariate online kernel density estimation with Gaussian kernels,
PR(44), No. 10-11, October-November 2011, pp. 2630-2642.
Elsevier DOI 1101
BibRef
Earlier: A1, A2, Only:
Online Discriminative Kernel Density Estimation,
ICPR10(581-584).
IEEE DOI 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],
Separation of CARS image contributions with a Gaussian mixture model,
JOSA-A(27), No. 6, June 2010, pp. 1361-1371.
WWW Link. 1006
BibRef

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.
Elsevier DOI 1003
Wishart mixture model; EM algorithm; Clustering; Second-order cross moments; Movement recognition BibRef

Rabbani, H., Gazor, S.,
Image denoising employing local mixture models in sparse domains,
IET-IPR(4), No. 5, October 2010, pp. 413-428.
DOI Link 1011
BibRef

Sun, J.Y.[Jian-Yong], Kaban, A.[Ata], Garibaldi, J.M.[Jonathan M.],
Robust mixture clustering using Pearson type VII distribution,
PRL(31), No. 16, December 2010, pp. 2447-2454.
Elsevier DOI 1011
Robust mixture modeling; Pearson type VII distribution; Outlier detection; Robust learning BibRef

Ulker, Y.[Yener], Gunsel, B.[Bilge], Cemgil, A.T.[Ali Taylan],
Annealed SMC Samplers for Nonparametric Bayesian Mixture Models,
SPLetters(18), No. 1, January 2011, pp. 3-6.
IEEE DOI 1011
BibRef
Earlier:
Annealed SMC Samplers for Dirichlet Process Mixture Models,
ICPR10(2808-2811).
IEEE DOI 1008
BibRef

Xie, C.H.[Cong-Hua], Song, Y.Q.[Yu-Qing], Chen, J.M.[Jian-Mei],
Fast medical image mixture density clustering segmentation using stratification sampling and kernel density estimation,
SIViP(5), No. 2, June 2011, pp. 257-267.
WWW Link. 1101
BibRef

Ma, Z.Y.[Zhan-Yu], Leijon, A.[Arne],
Bayesian Estimation of Beta Mixture Models with Variational Inference,
PAMI(33), No. 11, November 2011, pp. 2160-2173.
IEEE DOI 1110
BibRef
Earlier:
Beta mixture models and the application to image classification,
ICIP09(2045-2048).
IEEE DOI 0911
BibRef

Taghia, J., Ma, Z.Y.[Zhan-Yu], Leijon, A.[Arne],
Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference,
PAMI(36), No. 9, September 2014, pp. 1701-1715.
IEEE DOI 1408
Approximation methods BibRef

Ma, Z.Y.[Zhan-Yu], Rana, P.K.[Pravin Kumar], Taghia, J.[Jalil], Flierl, M.[Markus], Leijon, A.[Arne],
Bayesian estimation of Dirichlet mixture model with variational inference,
PR(47), No. 9, 2014, pp. 3143-3157.
Elsevier DOI 1406
Bayesian estimation BibRef

Taghia, J.[Jalil], Leijon, A.[Arne],
Variational Inference for Watson Mixture Model,
PAMI(38), No. 9, September 2016, pp. 1886-1900.
IEEE DOI 1609
Bayes methods BibRef

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.
Elsevier DOI 1201
Possibilistic clustering; Finite mixture models BibRef

Platanios, E.A., Chatzis, S.P.[Sotirios P.],
Gaussian Process-Mixture Conditional Heteroscedasticity,
PAMI(36), No. 5, May 2014, pp. 888-900.
IEEE DOI 1405
Bayes methods BibRef

Raitoharju, M., Ali-Loytty, S.,
An Adaptive Derivative Free Method for Bayesian Posterior Approximation,
SPLetters(19), No. 2, February 2012, pp. 87-90.
IEEE DOI 1201
BibRef

Browne, R.P.[Ryan P.], McNicholas, P.D.[Paul D.], Sparling, M.D.[Matthew D.],
Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions,
PAMI(34), No. 4, April 2012, pp. 814-817.
IEEE DOI 1203
BibRef

Franczak, B.C., Browne, R.P., McNicholas, P.D.,
Mixtures of Shifted Asymmetric Laplace Distributions,
PAMI(36), No. 6, June 2014, pp. 1149-1157.
IEEE DOI 1406
Algorithm design and analysis BibRef

Gallaugher, M.P.B.[Michael P.B.], McNicholas, P.D.[Paul D.],
Finite mixtures of skewed matrix variate distributions,
PR(80), 2018, pp. 83-93.
Elsevier DOI 1805
Clustering, Matrix variate, Mixture models, Skewed distributions BibRef

Andrews, J.L.[Jeffrey L.], McNicholas, P.D.[Paul D.],
Using evolutionary algorithms for model-based clustering,
PRL(34), No. 9, July 2013, pp. 987-992.
Elsevier DOI 1305
Cluster analysis; EM algorithm; Evolutionary algorithms; Finite mixture models; Model-based clustering BibRef

Tits, L., Somers, B., Coppin, P.,
The Potential and Limitations of a Clustering Approach for the Improved Efficiency of Multiple Endmember Spectral Mixture Analysis in Plant Production System Monitoring,
GeoRS(50), No. 6, June 2012, pp. 2273-2286.
IEEE DOI 1205
BibRef

Wang, Z., Lan, L., Vucetic, S.,
Mixture Model for Multiple Instance Regression and Applications in Remote Sensing,
GeoRS(50), No. 6, June 2012, pp. 2226-2237.
IEEE DOI 1205
BibRef

Yang, M.S.[Miin-Shen], Lai, C.Y.[Chien-Yo], Lin, C.Y.[Chih-Ying],
A robust EM clustering algorithm for Gaussian mixture models,
PR(45), No. 11, November 2012, pp. 3950-3961.
Elsevier DOI 1206
Cluster analysis; EM algorithm; Gaussian mixture model; Robust EM; Initialization; Number of clusters BibRef

Liu, G.Q.[Guo-Qing], Wu, J.X.[Jian-Xin], Zhou, S.P.[Sui-Ping],
Probabilistic classifiers with a generalized Gaussian scale mixture prior,
PR(46), No. 1, January 2013, pp. 332-345.
Elsevier DOI 1209
Classification; Prior distribution; Generalized Gaussian scale mixture; Likelihood function BibRef

Zhao, Q.P.[Qin-Pei], Hautamäki, V.[Ville], Kärkkäinen, I.[Ismo], Fränti, P.[Pasi],
Random swap EM algorithm for Gaussian mixture models,
PRL(33), No. 16, 1 December 2012, pp. 2120-2126.
Elsevier DOI 1210
BibRef
Earlier:
Random swap EM algorithm for finite mixture models in image segmentation,
ICIP09(2397-2400).
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],
Centroid index: Cluster level similarity measure,
PR(47), No. 9, 2014, pp. 3034-3045.
Elsevier DOI 1406
Clustering BibRef

Fränti, P.[Pasi], Rezaei, M.[Mohammad],
Generalizing Centroid Index to Different Clustering Models,
SSSPR16(285-296).
Springer DOI 1611
BibRef

Beaulieu, N.C.,
Special Values of the Bivariate Gaussian Distribution,
SPLetters(20), No. 1, January 2013, pp. 99-101.
IEEE DOI 1212
BibRef

Ali, A.M.[Asem M.], Farag, A.A., Alajlan, N., Farag, A.A.,
Multimodal imaging: modelling and segmentation with biomedical applications,
IET-CV(6), No. 6, 2012, pp. 524-539.
DOI Link 1301
BibRef

Ali, A.M.[Asem M.], Farag, A.A.[Amal A.], Farag, A.A.[Aly A.],
Labelling color images by modelling the colors density using a linear combination of Gaussians and EM algorithm,
ICIP09(1645-1648).
IEEE DOI 0911
BibRef

Mostafa, E., Ali, A.M.[Asem M.], Farag, A.A.[Aly A.],
Learning a non-linear combination of Mahalanobis distances using statistical inference for similarity measure,
IET-CV(9), No. 4, 2015, pp. 541-548.
DOI Link 1509
computer vision BibRef

Ali, A.M.[Asem M.], Farag, A.A.[Aly A.],
Density estimation using a new AIC-type criterion and the EM algorithm for a linear combination of Gaussians,
ICIP08(3024-3027).
IEEE DOI 0810
BibRef

Farag, A.A., El-Baz, A., Gimel'farb, G.L.,
Density estimation using modified expectation-maximization algorithm for a linear combination of gaussians,
ICIP04(III: 1871-1874).
IEEE DOI 0505
BibRef
And: A3, A1, A2:
Expectation-maximization for a linear combination of Gaussians,
ICPR04(III: 422-425).
IEEE DOI 0409
BibRef

Fan, W.T.[Wen-Tao], Bouguila, N.[Nizar],
Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection,
PR(46), No. 10, October 2013, pp. 2754-2769.
Elsevier DOI 1306
Infinite mixture models; Dirichlet process; Generalized Dirichlet; Feature selection; Clustering; Images categorization; Image auto-annotation BibRef

Fan, W.T.[Wen-Tao], 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],
WaveLBP based hierarchical features for image classification,
PRL(34), No. 12, 1 September 2013, pp. 1323-1328.
Elsevier DOI 1306
Image descriptor; Wavelet decomposition; Local binary pattern (LBP); Gaussian mixture model (GMM); Image classification BibRef

Hara, K., Inoue, K.[Kohei], Urahama, K.[Kiichi],
Generalized Mixture Ratio Based Blind Image Separation,
SPLetters(20), No. 8, 2013, pp. 743-746.
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,
PR(47), No. 4, 2014, pp. 1689-1697.
Elsevier DOI 1402
Pair-copula construction BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
Learning a mixture model for clustering with the completed likelihood minimum message length criterion,
PR(47), No. 5, 2014, pp. 2011-2030.
Elsevier DOI 1402
Completed likelihood BibRef

Li, D.W.[Da-Wei], Xu, L.H.[Li-Hong], Goodman, E.[Erik],
On-line EM Variants for Multivariate Normal Mixture Model in Background Learning and Moving Foreground Detection,
JMIV(48), No. 1, January 2014, pp. 114-133.
WWW Link. 1402
BibRef

Chai, J.[Jing], Chen, H.T.[Hong-Tao], Huang, L.X.[Li-Xia], Shang, F.H.[Fan-Hua],
Maximum margin multiple-instance feature weighting,
PR(47), No. 6, 2014, pp. 2091-2103.
Elsevier DOI 1403
Feature weighting BibRef

Hu, L.X.[Li-Xia],
A note on order statistics-based parametric pattern classification,
PR(48), No. 1, 2015, pp. 43-49.
Elsevier DOI 1410
Pattern classification BibRef

Zanotta, D.C., Haertel, V., Shimabukuro, Y.E., Renno, C.D.,
Linear Spectral Mixing Model for Identifying Potential Missing Endmembers in Spectral Mixture Analysis,
GeoRS(52), No. 5, May 2014, pp. 3005-3012.
IEEE DOI 1403
Correlation BibRef

Qu, Q., Nasrabadi, N.M., Tran, T.D.,
Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation,
GeoRS(52), No. 7, July 2014, pp. 4404-4423.
IEEE DOI 1403
Data models BibRef

Sun, X., Nasrabadi, N.M., Tran, T.D.,
Supervised Deep Sparse Coding Networks for Image Classification,
IP(29), No. 1, 2020, pp. 405-418.
IEEE DOI 1910
BibRef
Earlier:
Supervised Deep Sparse Coding Networks,
ICIP18(346-350)
IEEE DOI 1809
computational complexity, image classification, image coding, learning (artificial intelligence), neural nets, image recognition. Encoding, Dictionaries, Training, Optimization, Neural networks, Nonhomogeneous media, Computer architecture BibRef

Kersten, J.[Jens],
Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems,
PR(47), No. 8, 2014, pp. 2582-2595.
Elsevier DOI 1405
Gaussian mixture models BibRef

Zhang, R., Gong, W., Grzeda, V., Yaworski, A., Greenspan, M.,
Scene Dynamics Estimation for Parameter Adjustment of Gaussian Mixture Models,
SPLetters(21), No. 9, Sept 2014, pp. 1130-1134.
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,
JMIV(49), No. 3, July 2014, pp. 551-568.
Springer DOI 1407
Monotone inclusions involving mixtures of composite sum type operators. BibRef

Ari, C.[Caglar], Aksoy, S.[Selim],
Detection of Compound Structures Using a Gaussian Mixture Model With Spectral and Spatial Constraints,
GeoRS(52), No. 10, October 2014, pp. 6627-6638.
IEEE DOI 1407
BibRef
Earlier:
Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization,
ICPR10(746-749).
IEEE DOI 1008
Compounds BibRef

Xie, C.H.[Cong-Hua], Chang, J.Y.[Jin-Yi], Xu, W.B.[Wen-Bin],
Medical image denoising by generalised Gaussian mixture modelling with edge information,
IET-IPR(8), No. 8, August 2014, pp. 464-476.
DOI Link 1410
Bayes methods BibRef

Wang, B.H.[Bing-Hui], Lin, C.[Chuang], Fan, X.[Xin], Jiang, N.[Ning], Farina, D.[Dario],
Hierarchical Bayes based Adaptive Sparsity in Gaussian Mixture Model,
PRL(49), No. 1, 2014, pp. 238-247.
Elsevier DOI 1410
High-dimensional parameter estimation BibRef

Dedecius, K., Reichl, J., Djuric, P.M.,
Sequential Estimation of Mixtures in Diffusion Networks,
SPLetters(22), No. 2, February 2015, pp. 197-201.
IEEE DOI 1410
Bayes methods BibRef

Costa, M., Koivunen, V., Poor, H.V.,
Estimating Directional Statistics Using Wavefield Modeling and Mixtures of von-Mises Distributions,
SPLetters(21), No. 12, December 2014, pp. 1496-1500.
IEEE DOI 1410
Gaussian processes BibRef

Huang, S.M.[Shih-Ming], Chou, Y.T.[Yang-Ting], Yang, J.F.[Jar-Ferr],
Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models,
IET-CV(8), No. 5, October 2014, pp. 382-390.
DOI Link 1412
Gaussian processes BibRef

Kay, S.,
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Uncertainty, Feature extraction, Image recognition, Stochastic processes, Optimization, Bayes methods, mixture of gaussian mixture models BibRef

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IEEE DOI 2202
Detectors, Object detection, Hyperspectral imaging, Noise reduction, Mixture models, Interference, Deep learning, hyperspectral target detection BibRef

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

Jeon, Y.[Younghwan], Hwang, G.[Ganguk],
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IEEE DOI 2211
Detectors, Sparse matrices, Mixture models, Signal detection, Eigenvalues and eigenfunctions, Data models, asymptotic optimality BibRef

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Mixture models, Convergence, Symmetric matrices, Manifolds, Computational modeling, Topology, Optimization, manifold learning BibRef

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Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions,
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Data models, Bayes methods, Analytical models, Mixture models, Modeling, Tools, Task analysis, Axial data, Watson distribution, depth image BibRef

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Phase ambiguity resolution and mixed pixel detection in EDM with multiple modulation wavelengths,
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Elsevier DOI 2304
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Unsupervised Linear Component Analysis for a Class of Probability Mixture Models,
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Gaussian mixture models, First-order optimization, Manifold optimization BibRef

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Unsupervised incremental estimation of Gaussian mixture models with 1D split moves,
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Unsupervised learning, Gaussian mixture models, Model selection, Split and merge methods BibRef

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Fast Bootstrapping Nonparametric Maximum Likelihood for Latent Mixture Models,
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Signal processing algorithms, Generators, Approximation algorithms, Mixture models, Monte Carlo methods, two-stage algorithm BibRef

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Ensemble of Deep Variational Mixture Models for Unsupervised Clustering,
ICIP24(807-813)
IEEE DOI 2411
Temperature distribution, Uncertainty, Merging, Mixture models, Predictive models, Vectors, Entropy, Clustering, trustworthy machine learning BibRef

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

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

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

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

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

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

Martins, I.[Isabel], Carvalho, P.[Pedro], Corte-Real, L.[Luís], Alba-Castro, J.L.[José Luis],
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Springer DOI 1706
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Mezuman, E., Weiss, Y.,
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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)
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Bayes methods BibRef

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Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering,
ICPR14(1460-1465)
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Tsuchiya, C.[Chikao], Malisiewicz, T.[Tomasz], Torralba, A.B.[Antonio B.],
Exemplar Network: A Generalized Mixture Model,
ICPR14(598-603)
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Covariance Trees for 2D and 3D Processing,
CVPR14(556-563)
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Gaussian Mixture Models. Statistical image processing. bayesian a posteriori BibRef

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Gaussian Mixture Model Selection Using Multiple Random Subsampling with Initialization,
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ICIP12(2497-2500).
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AVSS12(300-305).
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ICPR10(511-514).
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CDP Mixture Models for Data Clustering,
ICPR10(637-640).
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ICPR10(4557-4560).
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Gaussian Mixture Modeling with Gaussian Process Latent Variable Models,
DAGM10(272-282).
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IASP10(492-495).
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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,
Learning07(1-8).
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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).
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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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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
BibRef

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
Hyperspectral Mixture Models, Mixed Pixels .

Last update:Nov 26, 2024 at 16:40:19