Multi-modal Image Retrieval for Search-based Image Annotation with RF
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Multi-modal Image Retrieval for Search-based Image Annotation with RF
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-18889S" target="_blank" >GA16-18889S: Big Data Analytics for Unstructured Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018)
ISBN
9781538668573
ISSN
—
e-ISSN
—
Number of pages
9
Pages from-to
52-60
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Taichung, TAIWAN
Event date
Dec 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000459863600009