메뉴 건너뛰기




Volumn 38, Issue 12, 2005, Pages 2351-2362

Active curve axis Gaussian mixture models

Author keywords

AcaG; AcaGMM; Active curve axis; EM; Finite mixture models; GMM; Unsupervised learning

Indexed keywords

ALGORITHMS; DATA REDUCTION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION;

EID: 25144519295     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.01.017     Document Type: Article
Times cited : (20)

References (37)
  • 3
    • 0030737323 scopus 로고    scopus 로고
    • Modeling the manifolds of images of handwritten digits
    • G. Hinton, P. Dayan, and M. Revow Modeling the manifolds of images of handwritten digits IEEE Trans. Neural Networks 8 1997 65 74
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 65-74
    • Hinton, G.1    Dayan, P.2    Revow, M.3
  • 4
    • 4544372566 scopus 로고    scopus 로고
    • HMM-based handwrittenword recognition: On the optimization of the number of states, training iterations and gaussian components
    • S. Günter, and H. Bunke HMM-based handwrittenword recognition: on the optimization of the number of states, training iterations and gaussian components Pattern Recognition 37 10 2004 2069 2079
    • (2004) Pattern Recognition , vol.37 , Issue.10 , pp. 2069-2079
    • Günter, S.1    Bunke, H.2
  • 5
    • 0042440720 scopus 로고    scopus 로고
    • Man-made structure detection in natural images using a causal multiscale random field
    • S. Kumar, M. Hebert, Man-made structure detection in natural images using a causal multiscale random field, in: CVPR, 2003.
    • (2003) CVPR
    • Kumar, S.1    Hebert, M.2
  • 7
    • 0032337237 scopus 로고    scopus 로고
    • Detecting features in spatial point processes with clutter via model-based clustering
    • A. Dasgupta, and A.E. Raftery Detecting features in spatial point processes with clutter via model-based clustering J. Am. Stat. Assoc. 93 1998 294 302
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 294-302
    • Dasgupta, A.1    Raftery, A.E.2
  • 8
    • 0033098507 scopus 로고    scopus 로고
    • Tracking color objects using adaptive mixture models
    • S. McKenna, Y. Raja, and S. Gong Tracking color objects using adaptive mixture models Image Vision Comput. 17 1999 225 231
    • (1999) Image Vision Comput. , vol.17 , pp. 225-231
    • McKenna, S.1    Raja, Y.2    Gong, S.3
  • 9
    • 0031188771 scopus 로고    scopus 로고
    • Probabilistic visual learning for object representation
    • B. Moghaddam, and A. Pentland Probabilistic visual learning for object representation PAMI 19 7 1997 696 710
    • (1997) PAMI , vol.19 , Issue.7 , pp. 696-710
    • Moghaddam, B.1    Pentland, A.2
  • 10
    • 84963864574 scopus 로고    scopus 로고
    • Improved information maximization based face and facial feature detection from real-time video and application in a multi-modal person identification system
    • Z. Xiong, Y. Chen, R. Wang, T.S. Huang, Improved information maximization based face and facial feature detection from real-time video and application in a multi-modal person identification system, in: ICMI, 2002.
    • (2002) ICMI
    • Xiong, Z.1    Chen, Y.2    Wang, R.3    Huang, T.S.4
  • 11
    • 4544284645 scopus 로고    scopus 로고
    • Waveform quantization of speech using gaussian mixture models
    • Proceedings - Speech Processing, New York
    • J. Samuelsson, Waveform quantization of speech using gaussian mixture models, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. I, Proceedings - Speech Processing, New York, 2004, pp. 165-168.
    • (2004) IEEE International Conference on Acoustics, Speech, and Signal Processing , vol.1 , pp. 165-168
    • Samuelsson, J.1
  • 13
    • 4544354704 scopus 로고    scopus 로고
    • Variational Bayesian feature selection for Gaussian mixture models
    • Proceedings - Speech Processing, New York
    • F. Valente, C. Wellekens, Variational Bayesian feature selection for Gaussian mixture models, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. I, Proceedings - Speech Processing, New York, 2004, pp. 513-516.
    • (2004) IEEE International Conference on Acoustics, Speech, and Signal Processing , vol.1 , pp. 513-516
    • Valente, F.1    Wellekens, C.2
  • 14
    • 3042548438 scopus 로고    scopus 로고
    • Time series classification using Gaussian mixture models of reconstructed phase spaces
    • R.J. Povinelli, M.T. Johnson, A.C. Lindgren, and J.J. Ye Time series classification using Gaussian mixture models of reconstructed phase spaces IEEE Trans. Knowledge Data Eng. 16 6 2004 779 783
    • (2004) IEEE Trans. Knowledge Data Eng. , vol.16 , Issue.6 , pp. 779-783
    • Povinelli, R.J.1    Johnson, M.T.2    Lindgren, A.C.3    Ye, J.J.4
  • 16
    • 0032634283 scopus 로고    scopus 로고
    • Adaptive background mixture models for real-time tracking
    • C. Stauffer, and W. Grimson Adaptive background mixture models for real-time tracking CVPR vol. 2 1999 246 252
    • (1999) CVPR , vol.2 , pp. 246-252
    • Stauffer, C.1    Grimson, W.2
  • 17
    • 0344034707 scopus 로고    scopus 로고
    • Statistical background subtraction for a mobile observer
    • E. Hayman, and J.-O. Eklundh Statistical background subtraction for a mobile observer ICCV 2003
    • (2003) ICCV
    • Hayman, E.1    Eklundh, J.-O.2
  • 19
    • 25144465032 scopus 로고    scopus 로고
    • A statical modeling approach to content based retrieval
    • S. Basu, M. Naphade, and J.R. Smith A statical modeling approach to content based retrieval IEEE ICASSP 2002
    • (2002) IEEE ICASSP
    • Basu, S.1    Naphade, M.2    Smith, J.R.3
  • 20
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? which clustering method? - Answers via model-based cluster analysis
    • C. Fraley, and A.E. Raftery How many clusters? which clustering method? - answers via model-based cluster analysis Comput. J. 41 1998 578 588
    • (1998) Comput. J. , vol.41 , pp. 578-588
    • Fraley, C.1    Raftery, A.E.2
  • 21
    • 0242657969 scopus 로고    scopus 로고
    • Competitive em algorithm for finite mixture models
    • B. Zhang, C. Zhang, and X. Yi Competitive EM algorithm for finite mixture models Pattern Recognition 37 1 2004 131 144
    • (2004) Pattern Recognition , vol.37 , Issue.1 , pp. 131-144
    • Zhang, B.1    Zhang, C.2    Yi, X.3
  • 26
    • 33846255123 scopus 로고
    • Principal curves revisited
    • R. Tibshirani Principal curves revisited Stat. Comput. 2 1992 183 190
    • (1992) Stat. Comput. , vol.2 , pp. 183-190
    • Tibshirani, R.1
  • 27
  • 30
    • 0003857778 scopus 로고    scopus 로고
    • A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
    • UC Berkeley
    • J.A. Bilmes, A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models, Technical Report ICSI TR-97-021, UC Berkeley, 1997.
    • (1997) Technical Report , vol.ICSI TR-97-021
    • Bilmes, J.A.1
  • 31
    • 0002629270 scopus 로고
    • Maximum-likelihood from incomplete data via the em algorithm
    • A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum-likelihood from incomplete data via the EM algorithm J. Roy. Stat. Soc. B 39 1977 1 38
    • (1977) J. Roy. Stat. Soc. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 33
    • 0035575419 scopus 로고    scopus 로고
    • Accelerating em for large databases
    • B. Thiesson, C. Meek, and D. Heckerman Accelerating EM for large databases Mach. Learning 45 3 2001 279 299
    • (2001) Mach. Learning , vol.45 , Issue.3 , pp. 279-299
    • Thiesson, B.1    Meek, C.2    Heckerman, D.3
  • 34
    • 2642550607 scopus 로고    scopus 로고
    • Speeding up the em algorithm for mixture model-based segmentation of magnetic resonance images
    • S.-K. Ng, and G.J. McLachlan Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images Pattern Recognition 37 8 2004 1573 1589
    • (2004) Pattern Recognition , vol.37 , Issue.8 , pp. 1573-1589
    • Ng, S.-K.1    McLachlan, G.J.2
  • 36
    • 77956889087 scopus 로고
    • Reversible Jump Markov Chain Monte Carlo computation and bayesian model determination
    • P. Green Reversible Jump Markov Chain Monte Carlo computation and bayesian model determination Biometrika 82 1995 711 732
    • (1995) Biometrika , vol.82 , pp. 711-732
    • Green, P.1
  • 37
    • 0035422134 scopus 로고    scopus 로고
    • Minimum-entropy data partitioning using reversible jump Markov Chain Monte Carlo
    • S. Roberts, C. Holmes, and D. Denison Minimum-entropy data partitioning using reversible jump Markov Chain Monte Carlo IEEE Trans. Pattern Anal. Mach. Intell. 23 8 2001 909 914
    • (2001) IEEE Trans. Pattern Anal. Mach. Intell. , vol.23 , Issue.8 , pp. 909-914
    • Roberts, S.1    Holmes, C.2    Denison, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.