13.3.12.7 Hidden Markov Models, General Problems, Computation, Use

Chapter Contents (Back)
HMM. Hidden Markov Models.
See also MRF Optimization, Energy Minimization.

Rabiner, L.R.,
A tutorial on Hidden Markov Models and selected applications in speech recognition,
PIEEE(77), No. 2, February 1989, pp. 257-286. BibRef 8902 Survey, HMM BibRef

Zhao, Y.[Yunxin], Zhuang, X.H.[Xin-Hua], Atlas, L.[Les], Anderson, L.[Lars],
Parameter Estimation and Restoration of Noisy Images Using Gibbs Distributions in Hidden Markov Models,
GMIP(54), No. 3, May 1992, pp. 187-197. BibRef 9205

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.
IEEE DOI 9710

See also Estimation of Fuzzy Gaussian Mixture and Unsupervised Statistical Image Segmentation. BibRef

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

Forchhammer, S., Rasmussen, T.S.,
Adaptive Partially Hidden Markov Models with Application to Bilevel Image Coding,
IP(8), No. 11, November 1999, pp. 1516-1526.
IEEE DOI 9911
BibRef

Pieczynski, W., Bouvrais, J., Michel, C.,
Estimation of Generalized Mixture in the Case of Correlated Sensors,
IP(9), No. 2, February 2000, pp. 308-312.
IEEE DOI 0003
BibRef

Li, X.L.[Xiao-Lin], Parizeau, M.[Marc], Plamondon, R.[Rejean],
Training Hidden Markov Models with Multiple Observations: A Combinatorial Method,
PAMI(22), No. 4, April 2000, pp. 371-377.
IEEE DOI 0006
BibRef

Kwong, S., Chau, C.W., Man, K.F., Tang, K.S.,
Optimisation of HMM topology and its model parameters by genetic algorithms,
PR(34), No. 2, February 2001, pp. 509-522.
Elsevier DOI 0011
BibRef

Inoue, M.[Masashi], Ueda, N.[Naonori],
Exploitation of unlabeled sequences in hidden Markov models,
PAMI(25), No. 12, December 2003, pp. 1570-1581.
IEEE Abstract. 0401
How to use unlabeled data in learning. BibRef

Abou-Moustafa, K.T., Cheriet, M., Suen, C.Y.,
On the structure of hidden Markov models,
PRL(25), No. 8, June 2004, pp. 923-931.
Elsevier DOI 0405
BibRef

Li, J.[Jie], Wang, J.X.[Jia-Xin], Zhao, Y.N.[Yan-Nan], Yang, Z.H.[Ze-Hong],
Self-adaptive design of hidden Markov models,
PRL(25), No. 2, January 2004, pp. 197-210.
Elsevier DOI 0401
BibRef

McCane, B.[Brendan], Caelli, T.M.[Terry M.],
Diagnostic tools for evaluating and updating hidden Markov models,
PR(37), No. 7, July 2004, pp. 1325-1337.
Elsevier DOI 0405
BibRef
Earlier: A2, A1:
Components analysis of hidden Markov models in computer vision,
CIAP03(510-515).
IEEE DOI 0310
How parameter and topology estimation contribute. BibRef

Dupont, P., Denis, F., Esposito, Y.,
Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms,
PR(38), No. 9, September 2005, 1349-1371.
Elsevier DOI 0506
BibRef

Li, H.Z.[Hao-Zheng], Liu, Z.Q.A.[Zhi-Qi-Ang], Zhu, X.H.[Xiang-Hua],
Hidden Markov models with factored Gaussian mixtures densities,
PR(38), No. 11, November 2005, pp. 2022-2031.
Elsevier DOI 0509
BibRef

Bicego, M.[Manuele], Castellani, U.[Umberto], Murino, V.[Vittorio],
A Hidden Markov Model approach for appearance-based 3D object recognition,
PRL(26), No. 16, December 2005, pp. 2588-2599.
Elsevier DOI 0512
BibRef

Ji, S.H.[Shi-Hao], Krishnapuram, B.[Balaji], Carin, L.[Lawrence],
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning,
PAMI(28), No. 4, April 2006, pp. 522-532.
IEEE DOI 0604
BibRef

Johansson, M., Olofsson, T.,
Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models,
SPLetters(14), No. 2, February 2007, pp. 129-132.
IEEE DOI 0703
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 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 DOI 0210
BibRef

Huda, S.[Shamsul], Yearwood, J.[John], Togneri, R.[Roberto],
A stochastic version of Expectation Maximization algorithm for better estimation of Hidden Markov Model,
PRL(30), No. 14, 15 October 2009, pp. 1301-1309.
Elsevier DOI 0909
Hidden Markov Model; Expectation Maximization; Speech recognition; Constraint-based Evolutionary Algorithm; Stochastic EM BibRef

Pyun, K., Lim, J., Gray, R.M.,
A Robust Hidden Markov Gauss Mixture Vector Quantizer for a Noisy Source,
IP(18), No. 7, July 2009, pp. 1385-1394.
IEEE DOI 0906
BibRef

Schwier, J.M., Brooks, R.R., Griffin, C., Bukkapatnam, S.,
Zero knowledge hidden Markov model inference,
PRL(30), No. 14, 15 October 2009, pp. 1273-1280.
Elsevier DOI 0909
Pattern recognition; Hidden Markov model; Pattern discovery BibRef

Lim, J., Pyun, K.,
Cost-Effective Hidden Markov Model-Based Image Segmentation,
SPLetters(16), No. 3, March 2009, pp. 172-175.
IEEE DOI 0903
BibRef

Ji, S.H.[Shi-Hao], Carin, L.[Lawrence],
Cost-sensitive feature acquisition and classification,
PR(40), No. 5, May 2007, pp. 1474-1485.
Elsevier DOI 0702
Cost-sensitive classification; Partially observable Markov decision processes (POMDP); Hidden Markov models (HMMs); Variational Bayes (VB) BibRef

Bali, N., Mohammad-Djafari, A.,
Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis,
IP(17), No. 2, February 2008, pp. 217-225.
IEEE DOI 0801
BibRef

Bali, N., Mohammad-Djafari, A., Mohammadpoor, A.,
Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images,
ICIP06(969-972).
IEEE DOI 0610
BibRef

Chatzis, S.P.[Sotirios P.], Kosmopoulos, D.I.[Dimitrios I.], Varvarigou, T.A.[Theodora A.],
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model,
PAMI(31), No. 9, September 2009, pp. 1657-1669.
IEEE DOI 0907
BibRef

Chatzis, S.P.[Sotirios P.], Kosmopoulos, D.I.[Dimitrios I.],
A variational Bayesian methodology for hidden Markov models utilizing Student's-t mixtures,
PR(44), No. 2, February 2011, pp. 295-306.
Elsevier DOI 1011
Hidden Markov models; Student's-t distribution; Variational Bayes; Speaker identification; Robotic task failure; Violence detection BibRef

Chatzis, S.P., Kosmopoulos, D.I.,
Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models,
CirSysVideo(22), No. 7, July 2012, pp. 1076-1086.
IEEE DOI 1208
BibRef

Chatzis, S.P.[Sotirios P.],
Hidden Markov Models with Nonelliptically Contoured State Densities,
PAMI(32), No. 12, December 2010, pp. 2297-2304.
IEEE DOI 1011
BibRef

Kosmopoulos, D.I.[Dimitrios I.], Chatzis, S.P.[Sotirios P.],
Robust Visual Behavior Recognition,
SPMag(27), No. 5, 2010, pp. 34-45.
IEEE DOI 1003
BibRef

Chatzis, S.P.[Sotirios P.], Tsechpenakis, G.[Gavriil],
The infinite Hidden Markov random field model,
ICCV09(654-661).
IEEE DOI 0909
BibRef

Chatzis, S.P.[Sotirios P.], Demiris, Y.F.[Yi-Fannis],
A reservoir-driven non-stationary hidden Markov model,
PR(45), No. 11, November 2012, pp. 3985-3996.
Elsevier DOI 1206
Hidden Markov model; Dirichlet process; Reservoir BibRef

Chatzis, S.P.[Sotirios P.], Kosmopoulos, D.I.[Dimitrios I.], Papadourakis, G.M.[George M.],
A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies,
ISVC14(II: 51-62).
Springer DOI 1501
BibRef

Chatzis, S.P.[Sotirios P.], Demiris, Y.F.[Yi-Fannis],
The Infinite-Order Conditional Random Field Model for Sequential Data Modeling,
PAMI(35), No. 6, June 2013, pp. 1523-1534.
IEEE DOI 1305
learning applications. nonparametric Bayesian approach for modeling label sequences. mean-field-like approximation of the model marginal likelihood. BibRef

Ji, S.H.[Shi-Hao], Watson, L.T.[Layne T.], Carin, L.[Lawrence],
Semisupervised Learning of Hidden Markov Models via a Homotopy Method,
PAMI(31), No. 2, February 2009, pp. 275-287.
IEEE DOI 0901
BibRef

Khreich, W.[Wael], Granger, E.[Eric], Miri, A.[Ali], Sabourin, R.[Robert],
Adaptive ROC-based ensembles of HMMs applied to anomaly detection,
PR(45), No. 1, January 2012, pp. 208-230.
Elsevier DOI 1109
Classification; Multi-classifier systems; Incremental learning; Adaptive systems; ROC; Information fusion; Hidden Markov models; Anomaly detection; Computer and network security BibRef

Zhu, H.[Hao], He, Z.S.[Zhong-Shi], Leung, H.,
Simultaneous Feature and Model Selection for Continuous Hidden Markov Models,
SPLetters(19), No. 5, May 2012, pp. 279-282.
IEEE DOI 1204
BibRef

Perduca, V., Nuel, G.,
Measuring the Influence of Observations in HMMs Through the Kullback-Leibler Distance,
SPLetters(20), No. 2, February 2013, pp. 145-148.
IEEE DOI 1302
BibRef

Henter, G.E.[Gustav Eje], Kleijn, W.B.[W. Bastiaan],
Picking up the pieces: Causal states in noisy data, and how to recover them,
PRL(34), No. 5, 1 April 2013, pp. 587-594.
Elsevier DOI 1303
Computational mechanics; Causal states; CSSR; Hidden Markov model; HMM; Learnability Markov model structure discovery. BibRef

Henter, G.E.[Gustav Eje], Kleijn, W.B.[W. Bastiaan],
Minimum Entropy Rate Simplification of Stochastic Processes,
PAMI(38), No. 12, December 2016, pp. 2487-2500.
IEEE DOI 1609
Density functional theory BibRef

Cavalin, P.R.[Paulo R.], Sabourin, R.[Robert], Suen, C.Y.[Ching Y.],
LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs,
PR(45), No. 9, September 2012, pp. 3544-3556.
Elsevier DOI 1206
Adaptive systems; Ensembles of classifiers; Incremental learning; Dynamic selection; Hidden Markov models BibRef

Cruz, R.M.O.[Rafael M.O.], Sabourin, R.[Robert], Cavalcanti, G.D.C.[George D.C.], Ren, T.I.[Tsang Ing],
META-DES: A dynamic ensemble selection framework using meta-learning,
PR(48), No. 5, 2015, pp. 1925-1935.
Elsevier DOI 1502
BibRef
Earlier: A1, A2, A3, Only:
On Meta-learning for Dynamic Ensemble Selection,
ICPR14(1230-1235)
IEEE DOI 1412
Ensemble of classifiers Accuracy BibRef

Lindberg, D.V., Omre, H.,
Inference of the Transition Matrix in Convolved Hidden Markov Models and the Generalized Baum-Welch Algorithm,
GeoRS(53), No. 12, December 2015, pp. 6443-6456.
IEEE DOI 1512
Bayes methods BibRef

Lemeire, J.[Jan], Cartella, F.[Francesco],
The Forward Procedure for HSMMs based on Expected Duration,
SPLetters(23), No. 8, August 2016, pp. 1116-1120.
IEEE DOI 1608
hidden semiMarkov models. approximation theory BibRef

Valera, I.[Isabel], Ruiz, F.J.R.[Francisco J.R.], Perez-Cruz, F.[Fernando],
Infinite Factorial Unbounded-State Hidden Markov Model,
PAMI(38), No. 9, September 2016, pp. 1816-1828.
IEEE DOI 1609
hidden Markov models BibRef

Mattila, R., Rojas, C.R., Krishnamurthy, V., Wahlberg, B.,
Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models,
SPLetters(24), No. 12, December 2017, pp. 1813-1817.
IEEE DOI 1712
Newton-Raphson method, convex programming, hidden Markov models, maximum likelihood estimation, probability, system identification BibRef

Souza, M.A.[Mariana A.], Cavalcanti, G.D.C.[George D.C.], Cruz, R.M.O.[Rafael M.O.], Sabourin, R.[Robert],
Online local pool generation for dynamic classifier selection,
PR(85), 2019, pp. 132-148.
Elsevier DOI 1810
Multiple classifier systems, Instance hardness, Pool generation, Dynamic classifier selection BibRef

Roy, A., Cruz, R.M.O.[Rafael M.O.], Sabourin, R.[Robert], Cavalcanti, G.D.C.[George D.C.],
Meta-regression based pool size prediction scheme for dynamic selection of classifiers,
ICPR16(216-221)
IEEE DOI 1705
Complexity theory, Computational modeling, Data models, Force, Prediction algorithms, Predictive models, Training BibRef

Palazón-González, V.[Vicente], Marzal, A.[Andrés], Vilar, J.M.[Juan Miguel],
On hidden Markov models and cyclic strings for shape recognition,
PR(47), No. 7, 2014, pp. 2490-2504.
Elsevier DOI 1404
BibRef
Earlier:
Cyclic Linear Hidden Markov Models for Shape Classification,
PSIVT07(152-165).
Springer DOI 0712
Hidden Markov models BibRef

Palazón-González, V.[Vicente], Marzal, A.[Andrés],
Cyclic Viterbi Score for Linear Hidden Markov Models,
IbPRIA07(II: 339-346).
Springer DOI 0706
BibRef

Soullard, Y.[Yann], Saveski, M.[Martin], Artières, T.[Thierry],
Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models,
PRL(37), No. 1, 2014, pp. 161-171.
Elsevier DOI 1402
Hidden Markov Models BibRef

Derrode, S.[Stéphane], Benyoussef, L.[Lamia], Pieczynski, W.[Wojciech],
Subsampling-based HMC parameter estimation with application to large datasets classification,
SIViP(8), No. 5, July 2014, pp. 873-882.
Springer DOI 1407
Hidden Markov chain models. BibRef

Bartolucci, F.[Francesco], Pandolfi, S.[Silvia],
Comment on the paper 'On the memory complexity of the forward-backward algorithm,',
PRL(38), No. 1, 2014, pp. 15-19.
Elsevier DOI 1402
Hidden Markov model
See also On the memory complexity of the forward-backward algorithm. BibRef

Lindberg, D.V., Omre, H.,
Blind Categorical Deconvolution in Two-Level Hidden Markov Models,
GeoRS(52), No. 11, November 2014, pp. 7435-7447.
IEEE DOI 1407
Approximation methods BibRef

Zhang, Z., Crawford, M.M.,
A Batch-Mode Regularized Multimetric Active Learning Framework for Classification of Hyperspectral Images,
GeoRS(55), No. 11, November 2017, pp. 6594-6609.
IEEE DOI 1711
Diversity reception, Hidden Markov models, Hyperspectral imaging, Measurement, Training, Uncertainty, Active learning (AL), batch mode, classification, hyperspectral data, metric learning BibRef

Zheng, Y., Jeon, B., Sun, L., Zhang, J., Zhang, H.,
Student's t-Hidden Markov Model for Unsupervised Learning Using Localized Feature Selection,
CirSysVideo(28), No. 10, October 2018, pp. 2586-2598.
IEEE DOI 1811
Hidden Markov models, Data models, Unsupervised learning, Bayes methods, Estimation, Clustering algorithms, Robustness, Bayesian variational learning BibRef

Yang, Y.[Yun], Jiang, J.M.[Jian-Min],
Bi-weighted ensemble via HMM-based approaches for temporal data clustering,
PR(76), No. 1, 2018, pp. 391-403.
Elsevier DOI 1801
Data clustering BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing], Liu, C.S.[Chang-Song], Wu, Y.[Ying],
Heteroscedastic Max-Min Distance Analysis for Dimensionality Reduction,
IP(27), No. 8, August 2018, pp. 4052-4065.
IEEE DOI 1806
BibRef
Earlier:
Heteroscedastic max-min distance analysis,
CVPR15(4539-4547)
IEEE DOI 1510
Covariance matrices, Dimensionality reduction, Hidden Markov models, Image processing, Minimization, Training, trace quotient BibRef

Khmag, A.[Asem], Al Haddad, S.A.R., Ramlee, R.A., Kamarudin, N.[Noraziahtulhidayu], Malallah, F.L.[Fahad Layth],
Natural image noise removal using nonlocal means and hidden Markov models in transform domain,
VC(34), No. 12, December 2018, pp. 1661-1675.
Springer DOI 1811
BibRef

Dridi, N., Hadzagic, M.,
Akaike and Bayesian Information Criteria for Hidden Markov Models,
SPLetters(26), No. 2, February 2019, pp. 302-306.
IEEE DOI 1902
Bayes methods, blind source separation, channel estimation, hidden Markov models, channel length, symbol detection, blind estimation BibRef

Chen, Y.K.[Yu-Kun], Ye, J.B.[Jian-Bo], Li, J.[Jia],
Aggregated Wasserstein Distance and State Registration for Hidden Markov Models,
PAMI(42), No. 9, September 2020, pp. 2133-2147.
IEEE DOI 2008
Distance between two Hidden Markov Models. Hidden Markov models, Monte Carlo methods, Gaussian distribution, Measurement, Computational modeling, Approximation methods, optimal transport BibRef

Chen, M.[Mulin], Wang, Q.[Qi], Li, X.L.[Xue-Long],
Discriminant Analysis with Graph Learning for Hyperspectral Image Classification,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef
And:
Robust Adaptive Sparse Learning Method for Graph Clustering,
ICIP18(1618-1622)
IEEE DOI 1809
Robustness, Manifolds, Linear programming, Clustering algorithms, Sparse matrices, Optimization, Toy manufacturing industry, Sparse Learning BibRef

He, F.[Fang], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Jia, W.M.[Wei-Min], Zhang, F.G.[Feng-Gan], Li, X.L.[Xue-Long],
Semisupervised Band Selection With Graph Optimization for Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10298-10311.
IEEE DOI 2112
Optimization, Analytical models, Hyperspectral imaging, Feature extraction, Hidden Markov models, Computational modeling, semisupervised BibRef

Hu, H.J.[Hao-Jie], Ding, Y.[Yao], He, F.[Fang], Zhang, F.G.[Feng-Gan], Zhao, J.W.[Jian-Wei], Yao, M.[Minli],
Bi-Kernel Graph Neural Network with Adaptive Propagation Mechanism for Hyperspectral Image Classification,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Fan, W.T.[Wen-Tao], Wang, R.[Ru], Bouguila, N.[Nizar],
Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models,
PR(119), 2021, pp. 108073.
Elsevier DOI 2106
Continuous hidden Markov models, Generalized inverted Dirichlet, Mixture models, Localized feature selection BibRef

Su, B.[Bing], Zhou, J.H.[Jia-Huan], Wen, J.R.[Ji-Rong], Wu, Y.[Ying],
Linear and Deep Order-Preserving Wasserstein Discriminant Analysis,
PAMI(44), No. 6, June 2022, pp. 3123-3138.
IEEE DOI 2205
BibRef
Earlier: A1, A2, A4, Only:
Order-Preserving Wasserstein Discriminant Analysis,
ICCV19(9884-9893)
IEEE DOI 2004
Hidden Markov models, Feature extraction, Dimensionality reduction, Joints, sequence classification. image classification, image motion analysis, image recognition, image representation, Prototypes BibRef

Cheng, X.[Xiang], Lei, H.[Hong],
Remote Sensing Scene Image Classification Based on mmsCNN-HMM with Stacking Ensemble Model,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
Modified Multi-Scale Convolution Neural Network with HMM. BibRef

Awiszus, M.[Maren], Rosenhahn, B.[Bodo],
Markov Chain Neural Networks,
Scarce18(2261-22617)
IEEE DOI 1812
Markov processes, Biological neural networks, Hidden Markov models, Biological system modeling, Games BibRef

Chen, Y.[Yukun], Ye, J.B.[Jian-Bo], Li, J.[Jia],
A Distance for HMMs Based on Aggregated Wasserstein Metric and State Registration,
ECCV16(VI: 451-466).
Springer DOI 1611
dissimilarity measure or distance between two Hidden Markov Models BibRef