About: Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 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. 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 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. (en) 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 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. (cs)
Title	Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors (en) Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors (cs)
skos:prefLabel	Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors (en) Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors (cs)
skos:notation	RIV/68407700:21230/08:03150834!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM6840770038)
http://linked.open...vai/riv/dodaniDat	2009
http://linked.open...aciTvurceVysledku	Matas, Jiří
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	376418
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/08:03150834
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	SIFT; computer vision; linear discrimant analysis (en)
http://linked.open.../riv/klicoveSlovo	linear discrimant analysis computer vision SIFT
http://linked.open...ontrolniKodProRIV	[F32272584FA4]
http://linked.open...v/mistoKonaniAkce	Leeds
http://linked.open...i/riv/mistoVydani	London
http://linked.open...i/riv/nazevZdroje	BMVC 2008: Proceedings of the 19th British Machine Vision Conference
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2008
http://linked.open...iv/tvurceVysledku	Matas, Jiří Mikolajczyk, K. Cai, H.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2008-09-01 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Decision Making and Control for Manufacturing III
number of pages	10 (xsd:int)
http://purl.org/ne...btex#hasPublisher	British Machine Vision Association
https://schema.org/isbn	978-1-901725-36-0
http://localhost/t...ganizacniJednotka	21230

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