A USE OF MEAN FIELD APPROXIMATION IN MEDIA INFORMATION PROCESSING USING MARKOV MODEL

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In media information processing such as image and speech processing, Markov models are commonly used to model observation process as well as hidden process. In such media processing, parameter estimation of probability density functions for both observation and hidden processes, probability computation of given observed image and speech, and estimation of hidden process need to be carried out. Efficient algorithms to carry out such estimation and computation have already been proposed for causal Markov models but not for noncausal ones. In this research project, efficient algorithms for noncausal Markov models have first been proposed which are realized using the mean field approximation. The proposed method is based on the fact that the probabilities of hidden process and observation process for a whole image, and even the a posteriori probability of hidden process given observation process are decomposed into the product of local pixelwise probabilities, using the mean field approximation. The local a posteriori vector, which is composed of local a posteriori probabilities for a set of hidden states, can be used as the mean field for each pixel. The proposed method was applied to real image and speech processing to evaluate its performance. In image processing, Markov random field (MRF) model was used and in particular a framework to model wavelet transformed images by the MRF model was investigated. Through texture classification and textured image segmentation, this approach is shown to be very effective to overcome the well-known problem in conventional modeling of original images where very short range interactions are only considered. In speech processing, the proposed method was applied to speaker recognition and is shown to be effective in online speaker verification and identification using the sequential probability ration test.