Learning linear discriminant projections for dimensionality reduction of image descriptors
Cai, H, Mikolajczyk, K and Matas, J (2008) Learning linear discriminant projections for dimensionality reduction of image descriptors In: Proceedings of the British Machine Vision Conference, 2008-09-01 - 2008-09-04, Leeds, UK.
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Abstract
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.
Item Type: | Conference or Workshop Item (Conference Paper) |
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Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing |
Authors : | Cai, H, Mikolajczyk, K and Matas, J |
Date : | 2008 |
DOI : | 10.5244/C.22.51 |
Additional Information : | Copyright 2008 The Authors |
Depositing User : | Symplectic Elements |
Date Deposited : | 15 Oct 2014 14:41 |
Last Modified : | 06 Jul 2019 05:14 |
URI: | http://epubs.surrey.ac.uk/id/eprint/806142 |
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