Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00175557" target="_blank" >RIV/68407700:21230/11:00175557 - isvavai.cz</a>

Výsledek na webu

<a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.89" target="_blank" >http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.89</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2010.89" target="_blank" >10.1109/TPAMI.2010.89</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Popis výsledku v původním jazyce

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 standarddata 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 st

Název v anglickém jazyce

Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

Popis výsledku anglicky

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 standarddata 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 st

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

338-352

Kód UT WoS článku

000285313200010

EID výsledku v databázi Scopus

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