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PAMI
2011

Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

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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.
Hongping Cai, Krystian Mikolajczyk, Jiri Matas
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where PAMI
Authors Hongping Cai, Krystian Mikolajczyk, Jiri Matas
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