Name
Abstract | ||
|---|---|---|
Canonical correlation analysis (CCA) is invariant with regard to affine transformation, but it cannot be directly applied to affine invariant pattern recognition. The reason mainly lies in that many existing CCA-based schemes represent the pattern by matrix-to-vector method, as a result, the structure and spatial information of the original pattern is discarded. In this paper, an affine invariant discriminate analysis (AIDA) method is developed for pattern recognition. Dislike the matrix-to-vector representation, an object is first converted to a projection matrix by central projection transform (CPT). After a point matching process, CCA is performed to projection matrices of the object and the model, and two vectors will be derived. Therefore, the object is classified to a model by the smallest distance between the obtained vectors. Comparisons of experimental results are given with respect to some existing methods, which demonstrate the effectiveness of the proposed AIDA method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neucom.2012.01.026 | Neurocomputing |
Keywords | Field | DocType |
Affine invariant discriminate analysis (AIDA),Canonical correlation analysis (CCA),Central projection transform (CPT),Affine transformation | Affine transformation,Affine shape adaptation,Harris affine region detector,Pattern recognition,Affine combination,Affine coordinate system,Artificial intelligence,Affine group,Affine hull,Mathematics,Affine geometry of curves | Journal |
Volume | Issue | ISSN |
86 | null | 0925-2312 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rushi Lan | 1 | 100 | 15.72 |
| Jianwei Yang | 2 | 58 | 12.73 |
| Yong Jiang | 3 | 1 | 0.35 |
| Zhan Song | 4 | 1 | 0.35 |
| Yuan Yan Tang | 5 | 2662 | 209.20 |