Dynamic Texture Classification Using Multivariate Hidden Markov Model

Abstract

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.