Search-based image annotation: Extracting semantics from similar images
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00081488" target="_blank" >RIV/00216224:14330/15:00081488 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-319-24027-5_36" target="_blank" >http://dx.doi.org/10.1007/978-3-319-24027-5_36</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-24027-5_36" target="_blank" >10.1007/978-3-319-24027-5_36</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Search-based image annotation: Extracting semantics from similar images
Popis výsledku v původním jazyce
The importance of automatic image annotation as a tool for handling large amounts of image data has been recognized for several decades. However, working tools have long been limited to narrow-domain problems with a few target classes for which precise models could be trained. With the advance of similarity searching, it now becomes possible to employ a different approach: extracting information from large amounts of noisy web data. However, several issues need to be resolved, including the acquisitionof a suitable knowledge base, choosing a suitable visual content descriptor, implementation of effective and efficient similarity search engine, and extraction of semantics from similar images. In this paper, we address these challenges and present a working annotation system based on the search-based paradigm, which achieved good results in the 2014 ImageCLEF Scalable Concept Image Annotation challenge.
Název v anglickém jazyce
Search-based image annotation: Extracting semantics from similar images
Popis výsledku anglicky
The importance of automatic image annotation as a tool for handling large amounts of image data has been recognized for several decades. However, working tools have long been limited to narrow-domain problems with a few target classes for which precise models could be trained. With the advance of similarity searching, it now becomes possible to employ a different approach: extracting information from large amounts of noisy web data. However, several issues need to be resolved, including the acquisitionof a suitable knowledge base, choosing a suitable visual content descriptor, implementation of effective and efficient similarity search engine, and extraction of semantics from similar images. In this paper, we address these challenges and present a working annotation system based on the search-based paradigm, which achieved good results in the 2014 ImageCLEF Scalable Concept Image Annotation challenge.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
—
Návaznosti výsledku
Projekt
<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2015
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
Experimental IR Meets Multilinguality, Multimodality, and Interaction - 6th International Conference of the CLEF Association, CLEF 2015
ISBN
9783319240268
ISSN
0302-9743
e-ISSN
—
Počet stran výsledku
13
Strana od-do
327-339
Název nakladatele
Springer
Místo vydání
Toulouse, France
Místo konání akce
Toulouse, France
Datum konání akce
1. 1. 2015
Typ akce podle státní příslušnosti
CST - Celostátní akce
Kód UT WoS článku
000364677800039