Video event classification and image segmentation based on noncausal multidimensional hidden Markov models

IEEE Transactions on Image Processing

Volumn 18, Issue 6, 2009, Pages 1304-1313

  Ma, Xiang ^a   Schonfeld, Dan ^a   Khokhar, Ashfaq A ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

Hidden Markov model (HMM); Image classification; Image segmentation; Motion trajectory analysis; Video classification

Indexed keywords

2-D MODEL; ACCURACY RATE; APPROXIMATE SOLUTION; CAUSAL SYSTEMS; CLASSIFICATION ALGORITHM; DISTRIBUTED COMPUTING; EXPECTATION MAXIMIZATION; HIDDEN MARKOV MODEL (HMM); MOTION TRAJECTORY ANALYSIS; MULTIPLE DIMENSIONS; NOVEL SOLUTIONS; SEQUENTIAL COMPUTING; SIMULATION RESULT; STATE TRANSITIONS; VIDEO CLASSIFICATION; VIDEO EVENTS;

COMPUTATIONAL COMPLEXITY; DIGITAL IMAGE STORAGE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; OBJECT RECOGNITION; SIMULATORS; VITERBI ALGORITHM; WAVELET TRANSFORMS;

HIDDEN MARKOV MODELS;

EID: 67349225720     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2009.2017166     Document Type: Article

References (24)

1. L. R. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
2. T. Starner and A. Pentland, Real-Time American Sign Language Recognition From Video Using Hidden Markov Models, MIT Media Lab., Perceptual Computing Group, Tech. Rep. 375, 1995.
3. F. I. Bashir, A.A. Khokhar, and D. Schonfeld, "Object trajectory-based activity classification and recognition using hidden Markov models," IEEE Trans. Image Process., vol. 16, no. 7, pp. 1912-1919, Jul. 2007.
4. J. Li, A. Najmi, and R. M. Gray, "Image classification by a two-dimensional hidden Markov model," IEEE Trans. Signal Process. vol. 48, no. 2, pp. 517-533, Feb. 2000.
5. X. Ma, A. Khokhar, and D. Schonfeld, "A general two-dimensional hidden Markov model and its application to image classification," in Proc. IEEE Int. Conf. Image Processing, San Antonio, TX, 2007, pp. 41-44.
6. H. C. Lin, L. L. Wang, and S. N. Yang, "Color image retrieval based on hidden Markov models," IEEE Trans. Image Process., vol. 6, no. 2, pp. 332-339, Feb. 1997.
7. C. Raphael, "Automatic segmentation of acoustic musical signals using hidden Markov models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 360-370, Apr. 1999.
8. A. V. Lukashin and M. Borodovsky, "GeneMark.hmm: New solutions for gene finding," Nucl. Acids Res., vol. 26, no. 4, pp. 1107-1115, 1998.
9. L. E. Baum and T. Petrie, "Statistical inference for probabilistic functions of finite state Markov chains," Ann. Math. Statist., vol. 37, no. 6, pp. 1554-1563, Dec. 1966.
10. S. S. Kuo and O. E. Agazzi, "Machine vision for keyword spotting using pseudo 2D hidden Markov models," in Proc. Int. Conf. Acoustic, Speech and Signal Processing, 1993, vol. 5, pp. 81-84.
11. M. Brand, N. Oliver, and A. Pentland, "Coupled hidden Markov models for complex action recognition," in Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 1997, p. 994.
12. P. A. Devijver, "Probabilistic labeling in a hidden second order Markov mesh," Pattern Recognit. Practice II, pp. 113-123, 1985.
13. K. Murphy, Y. Weiss, and M. I. Jordan, "Loopy belief propagation for approximate inference: An empirical study," presented at the Conf. Uncertainty in Artificial Intelligence, 1999.
14. J. M. Mooij and H. J. Kappen, "Sufficient conditions for convergence of loopy belief propagation," in Proc. 21st Annu. Conf. Uncertainty in Artificial Intelligence, Corvallis, OR, 2005, pp. 396-403.
15. N. Taga and S. Mase, "On the convergence of loopy belief propagation algorithm for different update rules," IEICE Trans. Fundam. Electron., Commun., Comput. Sci., vol. E89-A, no. 2, pp. 575-582, 2006.
16. M. J. Wainwright and M. I. Jordan, Graphical Models, Exponential Families, and Variational Inference Univ. California, Berkeley, Tech. Rep. 649, Sep. 2003, Dept. Statistics.
17. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the em algorithm," J. Roy. Statist. Soc. B, vol. 39, no. 1, pp. 1-38, 1977.
18. L. E. Baum, T. Petrie, G. Soules, and N.Weiss, "A maximization technique occuring in the statistical analysis of probabilitic functions of markov chains," Ann. Math. Statist., vol. 41, no. 1, pp. 164-171.
19. D. Schonfeld and N. Bouaynaya, "A new method for multidimensional optimization and its application in image and video processing," IEEE Signal Process. Lett., vol. 13, no. 8, pp. 485-488, Aug. 2006.
20. K. L. Oehler, R. M. Gray, J. A. Storer, and M. Cohn, "Combining image classification and image compression using vector quantization," in Proc. IEEE Data Compression Conf., Snowbird, UT, 1993, pp. 2-11.
21. K. Pyun, J. Lim, C. S. Won, and R. M. Gray, "Image segmentation using hidden Markov gauss mixture models," IEEE Trans. Image Process., vol. 16, no. 7, pp. 1902-1911, Jul. 2007.
22. L. Breiman, J. H. Freidman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA: Chapman & Hall, 1984.
23. T. Kohonen, Self-Organization and Associative Memory. Berlin, Germany: Springer-Verlag, 1989.
24. A. K. Aiyer, "Robust Image Compression Using Gauss Mixture Models," Ph.D. dissertation, Elect. Eng. Dept., Stanford Univ., Stanford, CA, Aug. 2001.