Publication detail
Annotating images with suggestions - user study of a tagging system
HRADIŠ, M. KOLÁŘ, M. KRÁL, J. LÁNÍK, A. ZEMČÍK, P. SMRŽ, P.
Original Title
Annotating images with suggestions - user study of a tagging system
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
This paper explores the concept of image-wise tagging. It introduces a web-baseduser interface for image annotation, and a novel method for modelingdependencies of tags using Restricted Boltzmann Machines which is able tosuggest probable tags for an image based on previously assigned tags. Accordingto our user study, our tag suggestion methods improve both user experience andannotation speed. Our results demonstrate that large datasets with semanticlabels (such as in TRECVID Semantic Indexing) can be annotated much moreefficiently with the proposed approach than with current class-domain-wisemethods, and produce higher quality data.
Keywords
Restricted Boltzmann Machine, human-assisted learning, user interface, image tagging, crowdsourcing, image classification
Authors
HRADIŠ, M.; KOLÁŘ, M.; KRÁL, J.; LÁNÍK, A.; ZEMČÍK, P.; SMRŽ, P.
RIV year
2012
Released
10. 7. 2012
Publisher
Springer Verlag
Location
Brno
ISBN
978-3-642-33139-8
Book
Advanced Concepts for Intelligent Vision Systems
Edition
Lecture Notes in Computer Science
ISBN
0302-9743
Periodical
Lecture Notes in Computer Science
Number
7517
State
Federal Republic of Germany
Pages from
155
Pages to
166
Pages count
12
URL
BibTex
@inproceedings{BUT96955,
author="Michal {Hradiš} and Martin {Kolář} and Jiří {Král} and Aleš {Láník} and Pavel {Zemčík} and Pavel {Smrž}",
title="Annotating images with suggestions - user study of a tagging system",
booktitle="Advanced Concepts for Intelligent Vision Systems",
year="2012",
series="Lecture Notes in Computer Science",
journal="Lecture Notes in Computer Science",
number="7517",
pages="155--166",
publisher="Springer Verlag",
address="Brno",
doi="10.1007/978-3-642-33140-4\{_}14",
isbn="978-3-642-33139-8",
issn="0302-9743",
url="https://www.fit.vut.cz/research/publication/9990/"
}
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