| Presentation | 2004/9/4 Synthesized-based learning for image recognition Hiroshi Murase, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper describes synthesis-based learning for image recognition. The synthesis-based learning method can improve the recognition accuracy even if we have a small set of learning samples. There are many ways to synthesize learning samples, such as adding noise, interpolation, generating function. This paper introduces several examples that authors applied in their recognition systems. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Image recognition / Learning sample / Subspace method |
| Paper # | PRMU2004-81 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2004/9/4(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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) | Synthesized-based learning for image recognition |
| Sub Title (in English) | |
| Keyword(1) | Image recognition |
| Keyword(2) | Learning sample |
| Keyword(3) | Subspace method |
| 1st Author's Name | Hiroshi Murase |
| 1st Author's Affiliation | Graduate School of Information Science, Nagoya University() |
| Date | 2004/9/4 |
| Paper # | PRMU2004-81 |
| Volume (vol) | vol.104 |
| Number (no) | 291 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |