| Project/Area Number |
10650370
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報通信工学
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
NODA Hideki Faculty of Engineering, Kyushu Institute of Technology Associate Professor, 工学部, 助教授 (80274554)
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| Co-Investigator(Kenkyū-buntansha) |
KAWAGUCHI Eiji Faculty of Engineering, Kyushu Institute of Technology Professor, 工学部, 教授 (90038000)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2000: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | 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 |
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| Research Abstract |
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.
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