Multi-modal Image Retrieval for Search-based Image Annotation with RF

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00101829" target="_blank" >RIV/00216224:14330/18:00101829 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ISM.2018.00017" target="_blank" >http://dx.doi.org/10.1109/ISM.2018.00017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISM.2018.00017" target="_blank" >10.1109/ISM.2018.00017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-modal Image Retrieval for Search-based Image Annotation with RF

Popis výsledku v původním jazyce

Search-based annotation methods can be used for proposing descriptive keywords to users who need to annotate images e.g. in image stock databases. From the annotation output, users select keywords which they want to assign to the given image. The selected keywords can serve as a relevance feedback for additional annotation refinement. In this paper, we study the possibilities of exploiting the annotation relevance feedback, which is a novel problem that has not been systematically addressed yet. In particular, we focus on the subtask of utilizing the feedback for the retrieval of related annotated images that are subsequently used for mining of candidate keywords. We select three multi-modal search techniques that can be applied to this problem, implement them within a state-of-the-art search-based annotation system, and experimentally evaluate their usefulness for annotation quality improvement.

Název v anglickém jazyce

Multi-modal Image Retrieval for Search-based Image Annotation with RF

Popis výsledku anglicky

Search-based annotation methods can be used for proposing descriptive keywords to users who need to annotate images e.g. in image stock databases. From the annotation output, users select keywords which they want to assign to the given image. The selected keywords can serve as a relevance feedback for additional annotation refinement. In this paper, we study the possibilities of exploiting the annotation relevance feedback, which is a novel problem that has not been systematically addressed yet. In particular, we focus on the subtask of utilizing the feedback for the retrieval of related annotated images that are subsequently used for mining of candidate keywords. We select three multi-modal search techniques that can be applied to this problem, implement them within a state-of-the-art search-based annotation system, and experimentally evaluate their usefulness for annotation quality improvement.

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

<a href="/cs/project/GA16-18889S" target="_blank" >GA16-18889S: Analytika pro velká nestrukturovaná data</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018)

ISBN

9781538668573

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

52-60

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Taichung, TAIWAN

Datum konání akce

10. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000459863600009