文献の詳細
| 論文の言語 | 英語 |
|---|---|
| 著者 | Tomohiro Sakata, Koichi Kise |
| 論文名 | Flower Classification by Using Multiple Kernel Learning |
| 論文誌名 | Proceedings of The 2nd China-Japan-Korea Joint Workshop on Pattern Recognition (CJKPR2010) |
| ページ | pp.144-147 |
| 査読の有無 | 有 |
| 年月 | 2010年11月 |
| 要約 | Object classification for categories with a significant visual similarity is a difficult problem. Because natural objects are slightly different for each individual, it is difficult to classify them with one feature. Therefore multiple features are needed for classification. As a method of combining multiple features, MKL has recently been focused. In this research, we employ color, shape, and texture features. We classify the flower images by using MKL and investigate the recognition rate. As a result, the best recognition rate is 75.66% in combining three features with flower 17 category dataset published by Visual Geometry Group of Oxford University. |
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- BibTeX用エントリー
@InProceedings{Sakata2010, author = {Tomohiro Sakata and Koichi Kise}, title = {Flower Classification by Using Multiple Kernel Learning}, booktitle = {Proceedings of The 2nd China-Japan-Korea Joint Workshop on Pattern Recognition (CJKPR2010)}, year = 2010, month = nov, pages = {144--147} }