Bayesian order-adaptive clustering for video segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4679 LNCS, Issue , 2007, Pages 334-349

  Orbanz, Peter ^a   Braendle, Samuel ^a   Buhmann, Joachim M ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; BAYESIAN NETWORKS; IMAGE CODING; IMAGE SEGMENTATION; TIME SERIES ANALYSIS;

BAYESIAN CLUSTERING PROBLEMS; MULTISCALE ALGORITHMS; NONPARAMETRIC BAYESIAN MODELS;

CLUSTERING ALGORITHMS;

EID: 38349005011     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74198-5_26     Document Type: Conference Paper

References (15)

1. Tekalp, A.M.: Video segmentation. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing, pp. 383-399 (2000)
2. Weiss, Y., Adelson, E.H.: A unified mixture framework for motion segmentation: Incorporating spatial coherence and estimating the number of models. In: Proc. of IEEE CVPR 1996 (1996)
3. Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Proc. of IEEE CVPR 1997 (1997)
4. Khan, S., Shah, M.: Object based segmentation of video, using color, motion and spatial information. In: Proc. of IEEE CVPR 2001 (2001)
5. Goldberger, J., Greenspan, H.: Context-based segmentation of image sequences. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(3), 463-468 (2006)
6. Bernardo, J.M., Smith, A.F.M.: Bayesian Theory. Wiley, Chichester (1994)
7. Ferguson, T.S.: A bayesian analysis of some nonparametric problems. Annals of Statistics 1(2) (1973)
8. Neal, R.M.: Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9, 249-265 (2000)
9. McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, Chichester (1997)
10. Stoica, P., Selen, Y.: Model order selection: A review of information criterion rules. IEEE Signal Processing, 36-47 (July 2004)
11. Lange, T., Roth, V., Braun, M., Buhmann, J.M.: Stability-based validation of clustering solutions. Neural Computation 16(6), 1299-1323 (2004)
12. Walker, S.G., Damien, P., Laud, P.W., Smith, A.F.M.: Bayesian nonparametric inference for random distributions and related functions. Journal of the Royal Statistical Society B 61(3), 485-527 (1999)
13. MacEachern, S.N.: Estimating normal means with a conjugate style Dirichlet process prior. Communications in Statistics: Simulation and Computation 23, 727-741 (1994)
14. Higdon, D., Lee, H., Holloman, C.: Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems. In: Bernardo, J.M., et al. (eds.) Bayesian Statistics vol. 7, pp. 181-198 (2003)
15. Orbanz, P., Buhmann, J.M.: Nonparametric Bayesian image segmentation. Int. J. of Computer Vision (to appear)