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

平均场近似在马尔可夫模型媒体信息处理中的应用

基本信息

批准号：
10650370
负责人：
NODA Hideki
金额：
$ 2.11万
依托单位：
Kyushu Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650370/
关键词：
Markov model media information mean field approximation Markov random field wavelet transform texture speaker recognition sequential probability ratio test 話者識別話者照合逐次決定 HMM 画像セグメンテーション EMアルゴリズム

项目摘要

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.

在图像和语音处理等媒体信息处理中，马尔可夫模型通常用于对观察过程和隐藏过程进行建模。在这种媒体处理中，需要进行观察过程和隐藏过程的概率密度函数的参数估计、给定观察图像和语音的概率计算以及隐藏过程的估计。已经针对因果马尔可夫模型提出了执行此类估计和计算的有效算法，但尚未针对非因果模型提出。在这个研究项目中，首先提出了使用平均场近似实现的非因果马尔可夫模型的有效算法。该方法基于以下事实：使用平均场近似，将整个图像的隐藏过程和观察过程的概率，甚至给定观察过程的隐藏过程的后验概率分解为局部像素概率的乘积。局部后验向量由一组隐藏状态的局部后验概率组成，可以用作每个像素的平均场。将所提出的方法应用于实际图像和语音处理以评估其性能。在图像处理中，使用马尔可夫随机场（MRF）模型，特别研究了通过MRF模型对小波变换图像进行建模的框架。通过纹理分类和纹理图像分割，这种方法被证明可以非常有效地克服原始图像传统建模中仅考虑非常短距离相互作用的众所周知的问题。在语音处理中，该方法被应用于说话人识别，并通过顺序概率比测试证明在在线说话人验证和识别中是有效的。