Parameter estimation of multi-dimensional hidden Markov models - a scalable approach

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.

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Dhiraj Joshi, Jia Li, James Ze Wang
• Keywords: ICIP 2005, Image Processing, Multi-dimensional Hidden Markov