Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel supervised CCA method for multi-view dimensionality reduction and classification, which simultaneously considers the class information of within-view and between-view training samples. The proposed method is applied to face and general object image recognition. The experimental results on the AT&T and Yale-B face image databases and the COIL-20 object image database show our proposed algorithm provides better recognition results on the whole than existing multiview feature extraction methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25417-3_82 | BIOMETRIC RECOGNITION, CCBR 2015 |
Keywords | Field | DocType |
Image recognition,Canonical Correlation Analysis,Dimensionality reduction,Supervised learning | Computer vision,External Data Representation,Dimensionality reduction,Pattern recognition,Canonical correlation,Computer science,Algorithm,Feature extraction,Supervised learning,Artificial intelligence,Image database | Conference |
Volume | ISSN | Citations |
9428 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun-Hao Yuan | 1 | 235 | 22.18 |
| Peng Lu | 2 | 2 | 0.36 |
| Zhiyong Xiao | 3 | 16 | 2.92 |
| Jianjun Liu | 4 | 46 | 6.47 |
| Xiaojun Wu | 5 | 356 | 52.89 |