Name
Title | ||
|---|---|---|
Towards fast and kernelized orthogonal discriminant analysis on person re-identification |
Abstract | ||
|---|---|---|
•We propose to solve the singularity problem in person re-identification by learning an orthogonal transformation with the pseudo-inverse of the within-class scatter matrix.•We develop a kernel version for learning the orthogonal transformation against the non-linear distribution of data in person re-identification, thereby boosting the performance of person re-identification.•We present a fast version with the unchanged performance of person reidentification for improving the solving efficiency.•We conduct experiments on four challenging datasets to demonstrates the validity and advantage of the proposed method for solving the singularity problem in person re-identification, and analyze the effectiveness of both kernel version and fast version. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.05.035 | Pattern Recognition |
Keywords | Field | DocType |
Person re-identification,Metric learning,Singularity problem,Orthogonal discriminant analysis | Kernel (linear algebra),Feature vector,Pattern recognition,Orthogonal transformation,Singularity,Artificial intelligence,Linear discriminant analysis,Discriminative model,Mathematics,Scatter matrix | Journal |
Volume | Issue | ISSN |
94 | 1 | 0031-3203 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||