2016 Fiscal Year Final Research Report
Structuring of un-labeled data by machine recognition and its applications
| Project/Area Number |
26330286
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Waseda University |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Keywords | 尤度最適化 / alpha-HMMアルゴリズム / alpha-EMアルゴリズム / 類似動画像検索 / 脳波認証 |
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| Outline of Final Research Achievements |
We addressed the structuring of unorganized information that causes information flood. The theory here is the optimization of the likelihood. The first aspect is the theoretical development of the likelihood maximization method. The second is to present novel applications by paying attention to the likelihood.
On the theoretical contribution, the alpha-HMM estimation method (hidden Markov model) was presented. This method includes the traditional log-HMM method (Baum-Welch method) as a special case showing a fast convergence. The second contribution is the alpha-EM method (expectation-maximization). We found shotgun optimization methods that are the fastest.On the applications, we addressed the similar-video retrieval. We extract exemplar frames that form a numerical label. By applying the M-distance (Matsuyama-Moriwaki distance), we succeeded in finding similar videos containing unauthorized scenes. We made another application on the fraudulent PIN holders with high performance.
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| Free Research Field |
情報学
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