| Presentation | 2007-03-16 Multi-class classifiers integrating local feature extracted by masking covariance matrix method Masashi TANIGAWA, Takio KURITA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a new method, MCM, Masking Covariance Matrix method, to improve the genelarization ability of classifiers, eventhough the sample size is not enough large. This method requires a prior knowledge such that an image data-set has a proper characteristic of the local-correlation among pixels. We examined the method, and showed an improvement for emotional face recognition task, using SVM classifiers. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | PRMU2006-273 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2007/3/9(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Pattern Recognition and Media Understanding (PRMU) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Multi-class classifiers integrating local feature extracted by masking covariance matrix method |
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| 1st Author's Name | Masashi TANIGAWA |
| 1st Author's Affiliation | Faculty of NeuroEngineering, Advanced Industrial Science and Technology() |
| 2nd Author's Name | Takio KURITA |
| 2nd Author's Affiliation | Faculty of NeuroEngineering, Advanced Industrial Science and Technology |
| Date | 2007-03-16 |
| Paper # | PRMU2006-273 |
| Volume (vol) | vol.106 |
| Number (no) | 606 |
| Page | pp.pp.- |
| #Pages | 4 |
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