Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition

Liu, Nianjun, Davis, Richard I. A., Lovell, Brian C. and Kootsookos, Peter J. (2004). Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition. In: P. Srimani, Proceedings of the International Conference on Information Technology: Coding and Computing. International Conference on Information Technology (ITCC 2004), Las Vegas, Nevada, U.S.A., (608-613). 5-7 April, 2004. doi:10.1109/ITCC.2004.1286531

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Author Liu, Nianjun
Davis, Richard I. A.
Lovell, Brian C.
Kootsookos, Peter J.
Title of paper Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition
Conference name International Conference on Information Technology (ITCC 2004)
Conference location Las Vegas, Nevada, U.S.A.
Conference dates 5-7 April, 2004
Proceedings title Proceedings of the International Conference on Information Technology: Coding and Computing
Journal name Itcc 2004: International Conference On Information Technology: Coding and Computing, Vol 1, Proceedings
Place of Publication Los Alamitos, California
Publisher The Institute of Electrical and Electronics Engineers Computer Society
Publication Year 2004
Sub-type Fully published paper
DOI 10.1109/ITCC.2004.1286531
Open Access Status File (Author Post-print)
ISBN 0-7695-2108-8
Editor P. Srimani
Volume 1
Start page 608
End page 613
Total pages 6
Language eng
Abstract/Summary We present several ways to initialize and train Hidden Markov Models (HMMs) for gesture recognition. These include using a single initial model for training (reestimation), multiple random initial models, and initial models directly computed from physical considerations. Each of the initial models is trained on multiple observation sequences using both Baum-Welch and the Viterbi Path Counting algorithm on three different model structures: Fully Connected (or ergodic), Left-Right, and Left-Right Banded. After performing many recognition trials on our video database of 780 letter gestures, results show that a) the simpler the structure is, the less the effect of the initial model, b) the direct computation method for designing the initial model is effective and provides insight into HMM learning, and c) Viterbi Path Counting performs best overall and depends much less on the initial model than does Baum-Welch training.
Subjects 280207 Pattern Recognition
E1
0899 Other Information and Computing Sciences
Keyword Iris-research
Gesture recognition
Hidden Markov models
References [1] L.R. Rabiner, and B.H. Juang, Fundamentals of Speech Recognition, New Jersey, Prentice Hall, 1993. [2] A. Kundu, Yang He and P. Bahl, "Recognition of Handwritten Words: First and Second Order Hidden Markov Model Based Approach," Pattern Recognition, vol. 22, no. 3, p. 283, 1989. [3] H. Bunke, T. Caelli, "Hidden Markov Models - Applications in Computer Vision," World Scientific Publishing Co, 2001. [4] T. Starner and A. Pentland, "Real-time American Sign Language Recognition," IEEE Trans, On Pattern Analysis and Machine Intelligence, Vol 20.pp 1371-1375, Dec. 1998. [5] Hyeon-Kyu Lee and Jin H. Kim, An HMM-Based Threshold Model Approach for Gesture Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence October 1999 (Vol. 21, No. 10). pp. 961-973. [6] J. Schlenzig, E. Hunter, and R. Jain. Recursive identification of gesture inputs using hidden markov models. In WACV94,pages 187-194, 1994. [7] R. I. A. Davis, and B. C. Lovell, and T. Caelli (2002) Improved Estimation of Hidden Markov Model Parameters from Multiple Observation Sequences. Proceedings of the International Conference on Pattern Recognition (ICPR2002), August 11-14 II, pp. 168-171, Quebec City, Canada. [8] R. I. A. Davis and B. C. Lovell, Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria. To Appear in the Journal of Pattern Analysis and Applications, 2003. [9] D. J. C. Mackay, Ensemble Learning for Hidden Markov Models, Technical report, Cavendish Laboratory, University of Cambridge, UK, 1997. [10] A. Stolcke and S. Omohundro. Hidden Markov Model induction by Bayesian model merging, Advances in Neural Information Processing Systems, pp. 11-18, Morgan Kaufmann, San Mateo, United States of America, CA1993. [11] Nianjun Liu, Brian C. Lovell and Peter J. Kootsookos, Evaluation of HMM training algorithms for Letter Hand Gesture Recognition, Proceedings of IEEE International Symposium on Signal Processing and Information Technology. December 14-17, 2003, Darmstadt, Germany. Accepted, In Press.
Q-Index Code E1

 
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Created: Wed, 23 Jun 2004, 10:00:00 EST by Brian C. Lovell on behalf of Faculty Of Engineering, Architecture & Info Tech