Abstract
The stochastic signal model, hidden Markov model (HMM), is a probabilistic function of the Markov chain. In this letter, we propose a general nth-order HMM based dynamic texture description and classification method. Specifically, the pixel intensity sequence along time of a dynamic texture is modeled with a HMM that encodes the appearance information of the dynamic texture with the observed variables, and the dynamic properties over time with the hidden states. A new dynamic texture sequence is classified to the category by determining whether it is the most similar to this category with the probability that the observed sequence is produced by the HMMs of the training samples. The experimental results demonstrate the arbitrary emission probability distribution and the higher-order dependence of hidden states of a higher-order HMM result in better classification performance, as compared with the linear dynamical system (LDS) based method.
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