Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
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Volume of the periodical
33
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
338-352
UT code for WoS article
000285313200010
EID of the result in the Scopus database
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