Name
Abstract | ||
|---|---|---|
Constructing a suitable distance metric for scene recognition is a very challenging task due to the huge intra-class variations. In this paper, we propose a novel framework for learning a full parameter matrix in Mahalanobis metric, where the learning process is formulated as a non-negatively constrained minimization problem in a projected space. To fully capture the structure of scenes, we first apply multiple regularized linear discriminant analysis (LDA) to form a candidate projection pool. Second, we adopt the pairwise squared differences of the projected samples as the learning instances. Finally, the diagonal selection matrix is learned through least squares with non-negative L2-norm regularization. Experiments on two datasets in scene recognition show the effectiveness and efficiency of our approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467562 | ICIP |
Keywords | Field | DocType |
least squares,metric learning,nonnegatively constrained minimization problem,nonnegative l2-norm regularization,learning (artificial intelligence),candidate projection pool,regularized lda,matrix algebra,learning instances,mahalanobis metric,image recognition,intraclass variations,least squares approximations,full parameter matrix,pairwise squared differences,multiple regularized linear discriminant analysis,scene recognition,diagonal selection matrix,minimisation,non-negative l2-norm regularization,mahalanobis distance metric,learning artificial intelligence | Least squares,Diagonal,Pairwise comparison,Pattern recognition,Matrix (mathematics),Computer science,Metric (mathematics),Mahalanobis distance,Regularization (mathematics),Artificial intelligence,Linear discriminant analysis | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |