A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition

@inproceedings{AvilsArriaga2011ACO,
  title={A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition},
  author={H{\'e}ctor Hugo Avil{\'e}s-Arriaga and L. E. Sucar-Succar and C. E. Mendoza-Dur{\'a}n and L. A. Pineda-Cort{\'e}s},
  year={2011}
}
In this paper we present a study to assess the performance of dynamic naive Bayesian classifiers (DNBCs) versus standard hidden Markov models (HMMs) for gesture recognition. DNBCs incorporate explicit conditional independence among gesture features given states into HMMs. We show that this factorization offers competitive classification rates and error dispersion, it requires fewer parameters and it improves training time considerably in the presence of several attributes. We propose a set of… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 66 references

Hidden Markov Models Software, Available at: http://www.kanungo.com

T. Kanungo
Last retrieved: May • 2008
View 2 Excerpts

Incremental and adaptive abnormal behaviour detection

Computer Vision and Image Understanding • 2008
View 1 Excerpt

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