Parameter Estimation of Multi-dimensional Hidden Markov Models
- A Scalable Approach
Dhiraj Joshi, Jia Li, James Z. Wang
The Pennsylvania State University
Parameter estimation is a key computational issue in all statistical
image modeling techniques. In this paper, we explore a
computationally efficient parameter estimation algorithm for
multi-dimensional hidden Markov models. 2-D HMM has been applied to
supervised aerial image classification and comparisons have been made
with the first proposed estimation algorithm. An extensive parametric
study has been performed with 3-D HMM and the scalability of the
estimation algorithm has been discussed. Results show the great
applicability of the explored algorithm to multi-dimensional HMM based
image modeling applications.
Full Paper in Color
Jia Li, Dhiraj Joshi and James Z. Wang, ``Parameter Estimation of Multi-dimensional Hidden Markov Models - A Scalable Approach,'' Proc. IEEE
International Conference on Image Processing (ICIP), Genova, Italy,
vol. 3, pp. 149-152, IEEE, September 2005.
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May 8, 2005