Automatic Image Labelling using Similarity Measures

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110448" target="_blank" >RIV/00216305:26220/14:PU110448 - isvavai.cz</a>
Výsledek na webu
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7005984&refinements%3D4273474444%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A7005558%29" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7005984&refinements%3D4273474444%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A7005558%29</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MedCom.2014.7005984" target="_blank" >10.1109/MedCom.2014.7005984</a>

Jazyk výsledku
angličtina
Název v původním jazyce
Automatic Image Labelling using Similarity Measures
Popis výsledku v původním jazyce
Scene classification based on global features. It can be used, for example, for annotating large databases of photos. The whole process has several steps. The first step is features extraction, and then the distance between a new image and reference images is calculated. A model is trained to classify new images based on this distance. The model was created using the Naïve Bayes classifier. To improve accuracy the forward selection was used, which optimizes the selection of a group of attributes. The overall performance on the testing dataset was 69.76%.
Název v anglickém jazyce
Automatic Image Labelling using Similarity Measures
Popis výsledku anglicky
Scene classification based on global features. It can be used, for example, for annotating large databases of photos. The whole process has several steps. The first step is features extraction, and then the distance between a new image and reference images is calculated. A model is trained to classify new images based on this distance. The model was created using the Naïve Bayes classifier. To improve accuracy the forward selection was used, which optimizes the selection of a group of attributes. The overall performance on the testing dataset was 69.76%.

Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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