Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03150834" target="_blank" >RIV/68407700:21230/08:03150834 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Original language description

This paper proposes a general method for improving image descriptors using discriminant projections. Two methods based on Linear Discriminant Analysis have been recently introduced to improve matching performance of local descriptors and to reduce theirdimensionality. 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 invarious application scenarios. The results validate the proposed method in viewpoint invariant matching and category recognition.

Czech name

Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Czech description

This paper proposes a general method for improving image descriptors using discriminant projections. Two methods based on Linear Discriminant Analysis have been recently introduced to improve matching performance of local descriptors and to reduce theirdimensionality. 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 invarious application scenarios. The results validate the proposed method in viewpoint invariant matching and category recognition.

Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2008

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

BMVC 2008: Proceedings of the 19th British Machine Vision Conference

ISBN

978-1-901725-36-0

ISSN

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e-ISSN

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Number of pages

10

Pages from-to

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Publisher name

British Machine Vision Association

Place of publication

London

Event location

Leeds

Event date

Sep 1, 2008

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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