Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

This paper proposes a general method for improving image descriptors using discriminant projections. Two methods based on Linear Discriminant Analysis have been recently introduced in [3, 11] to improve matching performance of local descriptors and to reduce their dimensionality. These methods require large training set with ground truth of accurate point-to-point correspondences which limits their applicability. We demonstrate the theoretical equivalence of these methods and provide a means to derive projection vectors on data without available ground truth. It makes it possible to apply this technique and improve performance of any combination of interest point detectors-descriptors. We conduct an extensive evaluation of the discriminative projection methods in various application scenarios. The results validate the proposed method in viewpoint invariant matching and category recognition.

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Hongping Cai, Krystian Mikolajczyk, Jiri Matas
• Keywords: Discriminative Projection Methods, Ground Truth, Linear Discriminant Analysis, Operations Research, PAMI 2011