FCA as a tool for inaccuracy detection in content-based image analysis
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F10%3A86085133" target="_blank" >RIV/61989100:27240/10:86085133 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1109/GrC.2010.24" target="_blank" >http://dx.doi.org/10.1109/GrC.2010.24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/GrC.2010.24" target="_blank" >10.1109/GrC.2010.24</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
FCA as a tool for inaccuracy detection in content-based image analysis
Popis výsledku v původním jazyce
In this paper we focus on the detection of inaccuracies in the results of content-based image analysis. During the analysis process we detect a set of features, which are later used in Image Retrieval. This detection is based on multiple algorithms specific to particular features. These algorithms use parameters, which have been obtained by the analysis of our test collection. However it seems that in the real application deployment produces some inaccuracies in the results. Our goal is to support the process of feature analysis by detecting these inaccuracies, or at least showing the most probable sources of them. This support can be helpful in tuning these algorithms on less known input data. In the article we describe both the image features detection algorithms as well as usage of Formal Concept Analysis (FCA) as a tool for detection of inaccuracies.
Název v anglickém jazyce
FCA as a tool for inaccuracy detection in content-based image analysis
Popis výsledku anglicky
In this paper we focus on the detection of inaccuracies in the results of content-based image analysis. During the analysis process we detect a set of features, which are later used in Image Retrieval. This detection is based on multiple algorithms specific to particular features. These algorithms use parameters, which have been obtained by the analysis of our test collection. However it seems that in the real application deployment produces some inaccuracies in the results. Our goal is to support the process of feature analysis by detecting these inaccuracies, or at least showing the most probable sources of them. This support can be helpful in tuning these algorithms on less known input data. In the article we describe both the image features detection algorithms as well as usage of Formal Concept Analysis (FCA) as a tool for detection of inaccuracies.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
Projekt
<a href="/cs/project/GA201%2F09%2F0990" target="_blank" >GA201/09/0990: Zpracování XML dat</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2010
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2010 IEEE International Conference on Granular Computing (GrC) : proceedings
ISBN
978-0-7695-4161-7
ISSN
—
e-ISSN
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Počet stran výsledku
6
Strana od-do
223-228
Název nakladatele
IEEE
Místo vydání
New York
Místo konání akce
San Jose
Datum konání akce
14. 8. 2010
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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