Robust image classification based on a non-causal hidden Markov Gauss mixture model

We propose a novel image classification method using a noncausal hidden Markov Gauss mixture model (HMGMM). We apply supervised learning assuming that the observation probability distribution given each class can be estimated using Gauss mixture vector quantization (GMVQ) designed using the generalized Lloyd algorithm with a minimum discrimination information (MDI) distortion. The maximum a posteriori (MAP) hidden states in an Ising model are estimated by a stochastic EM algorithm. We demonstrate that HMGMM obtains better classification than several popular methods, including CART, LVQ, causal HMM, and multi-resolution HMM in terms of Bayes risk and the spatial homogeneity of the classified objects. A heuristic solution for the number of cluster achieves a robust image classification.