抄録
Similar-image retrieval systems are presented and evaluated. The new systems directly use image bases via ICA (Independent Component Analysis) and PCA (Principal Component Analysis). These bases can extract source image's information which is viable to define similarity measures. But, the indeterminacy on amplitude and permutation exists. In this paper, similarity measures which can absorb such indeterminacy are presented. Then, carefully designed opinion tests are carried out to compare the new systems' ability with existing ones. The compatibility of color spaces such as RGB, YIQ, and HSV is also examined. By these massive tests, {ICA, HSV} is judged the best. The resulting system is thus proved to be highly competent at the similar-image retrieval.
| 本文言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings of the International Joint Conference on Neural Networks |
| ページ | 1229-1234 |
| ページ数 | 6 |
| 巻 | 2 |
| DOI | |
| 出版ステータス | Published - 2005 |
| イベント | International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC 継続期間: 2005 7月 31 → 2005 8月 4 |
Other
| Other | International Joint Conference on Neural Networks, IJCNN 2005 |
|---|---|
| City | Montreal, QC |
| Period | 05/7/31 → 05/8/4 |
ASJC Scopus subject areas
- ソフトウェア