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.

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Citation: 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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Last Modified: May 8, 2005