TY - GEN
T1 - Parameter estimation of multi-dimensional hidden Markov models - A scalable approach
AU - Joshi, Dhiraj
AU - Li, Jia
AU - Wang, James
PY - 2005/12/1
Y1 - 2005/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33749256666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749256666&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2005.1530350
DO - 10.1109/ICIP.2005.1530350
M3 - Conference contribution
AN - SCOPUS:33749256666
SN - 0780391349
SN - 9780780391345
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 149
EP - 152
BT - IEEE International Conference on Image Processing 2005, ICIP 2005
T2 - IEEE International Conference on Image Processing 2005, ICIP 2005
Y2 - 11 September 2005 through 14 September 2005
ER -