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

D 2018

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

BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

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

Autoři

BUDÍKOVÁ, Petra (203 Česká republika, garant, domácí), Michal BATKO (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

NEW YORK, 2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018), od s. 52-60, 9 s. 2018

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/18:00101829

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-6857-3

DOI

http://dx.doi.org/10.1109/ISM.2018.00017

UT WoS

000459863600009

Klíčová slova anglicky

image annotation; relevance feedback; multi-modal image retrieval

Štítky

DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 4. 2019 06:57, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

GA16-18889S, projekt VaV

Název: Analytika pro velká nestrukturovaná data (Akronym: Big Data Analytics for Unstructured Data)

Investor: Grantová agentura ČR, Big Data Analytics for Unstructured Data

Citovat

BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA. Multi-modal Image Retrieval for Search-based Image Annotation with RF. Online. In 2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018). NEW YORK: IEEE, 2018, s. 52-60. ISBN 978-1-5386-6857-3. Dostupné z: https://dx.doi.org/10.1109/ISM.2018.00017.

@inproceedings{1524636,
   author = {Budíková, Petra and Batko, Michal and Zezula, Pavel},
   address = {NEW YORK},
   booktitle = {2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018)},
   doi = {http://dx.doi.org/10.1109/ISM.2018.00017},
   keywords = {image annotation; relevance feedback; multi-modal image retrieval},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {NEW YORK},
   isbn = {978-1-5386-6857-3},
   pages = {52-60},
   publisher = {IEEE},
   title = {Multi-modal Image Retrieval for Search-based Image Annotation with RF},
   year = {2018}
}

TY  - JOUR
ID  - 1524636
AU  - Budíková, Petra - Batko, Michal - Zezula, Pavel
PY  - 2018
TI  - Multi-modal Image Retrieval for Search-based Image Annotation with RF
PB  - IEEE
CY  - NEW YORK
SN  - 9781538668573
KW  - image annotation
KW  - relevance feedback
KW  - multi-modal image retrieval
N2  - 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.
ER  -

BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA. Multi-modal Image Retrieval for Search-based Image Annotation with RF. Online. In \textit{2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018)}. NEW YORK: IEEE, 2018, s.~52-60. ISBN~978-1-5386-6857-3. Dostupné z: https://dx.doi.org/10.1109/ISM.2018.00017.

Podrobný výpis o publikaci