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Learning linear discriminant projections for dimensionality reduction of image descriptors

Cai, H, Mikolajczyk, K and Matas, J (2011) Learning linear discriminant projections for dimensionality reduction of image descriptors IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2). pp. 338-352.

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In this paper, we present Linear Discriminant Projections (LDP) for reducing dimensionality and improving discriminability of local image descriptors. We place LDP into the context of state-of-the-art discriminant projections and analyze its properties. LDP requires a large set of training data with point-to-point correspondence ground truth. We demonstrate that training data produced by a simulation of image transformations leads to nearly the same results as the real data with correspondence ground truth. This makes it possible to apply LDP as well as other discriminant projection approaches to the problems where the correspondence ground truth is not available, such as image categorization. We perform an extensive experimental evaluation on standard data sets in the context of image matching and categorization. We demonstrate that LDP enables significant dimensionality reduction of local descriptors and performance increases in different applications. The results improve upon the state-of-the-art recognition performance with simultaneous dimensionality reduction from 128 to 30.

Item Type: Article
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 : 2011
DOI : 10.1109/TPAMI.2010.89
Additional Information : Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User : Symplectic Elements
Date Deposited : 24 Jul 2012 10:37
Last Modified : 06 Jul 2019 05:10

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