DocumentCode
3098451
Title
A general parameter updating approach to image classification
Author
Jiang, Hongtao ; Bølviken, Erik
Author_Institution
Dept. of Inf., Oslo Univ., Norway
Volume
1
fYear
1994
fDate
9-13 Oct 1994
Firstpage
720
Abstract
This paper presents an EM approach to parameter updating in supervised image classification based on the maximum aposteriori (MAP) estimation. By specifying suitable prior distribution in the form of constraint on the differences between class mean vectors, the new algorithm generally gives better estimates of the class means than the maximum likelihood-EM algorithm, as shown by results with MR images of human brain
Keywords
image classification; EM approach; NMR imaging; class mean vectors; human brain image; image classification; maximum aposteriori estimation; maximum likelihood estimation; parameter updating; Bayesian methods; Covariance matrix; Humans; Image classification; Informatics; Iterative methods; Maximum likelihood estimation; Parameter estimation; Stochastic processes; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on
Conference_Location
Jerusalem
Print_ISBN
0-8186-6265-4
Type
conf
DOI
10.1109/ICPR.1994.576417
Filename
576417
Link To Document