Image classification based on a multiresolution two dimensional hidden Markov model

This paper presents an image classification algorithm based upon a two dimensional multiresolution hidden Markov model (MHMM). This model represents an image by feature vectors in several resolutions and considers the feature vectors statistically dependent through an underlying state process assumed to be a multiscale Markov mesh. To estimate the model by the maximum likelihood criterion, approximations are made successively based on the EM algorithm to reach feasible computation. To classify an image, the algorithm attempts to find the optimal set of states with the maximum a posteriori probability. The states are then mapped into classes. The multiresolution model enables multiscale context information to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification which greatly speeds up classification based on single resolution HMMs.