A general parameter updating approach to image classification

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.