Detail publikace

Annotating images with suggestions - user study of a tagging system

Originální název

Annotating images with suggestions - user study of a tagging system

Anglický název

Annotating images with suggestions - user study of a tagging system

Jazyk

en

Originální abstrakt

This paper explores the concept of image-wise tagging. It introduces a web-based user interface for image annotation, and a novel method for modeling dependencies of tags using Restricted Boltzmann Machines which is able to suggest probable tags for an image based on previously assigned tags. According to our user study, our tag suggestion methods improve both user experience and annotation speed. Our results demonstrate that large datasets with semantic labels (such as in TRECVID Semantic Indexing) can be annotated much more efficiently with the proposed approach than with current class-domain-wise methods, and produce higher quality data.

Anglický abstrakt

This paper explores the concept of image-wise tagging. It introduces a web-based user interface for image annotation, and a novel method for modeling dependencies of tags using Restricted Boltzmann Machines which is able to suggest probable tags for an image based on previously assigned tags. According to our user study, our tag suggestion methods improve both user experience and annotation speed. Our results demonstrate that large datasets with semantic labels (such as in TRECVID Semantic Indexing) can be annotated much more efficiently with the proposed approach than with current class-domain-wise methods, and produce higher quality data.

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",
  annote="This paper explores the concept of image-wise tagging. It introduces a web-based
user interface for image annotation, and a novel method for modeling dependencies
of tags using Restricted Boltzmann Machines which is able to suggest probable
tags for an image based on previously assigned tags. According to our user study,
our tag suggestion methods improve both user experience and annotation speed. Our
results demonstrate that large datasets with semantic labels (such as in TRECVID
Semantic Indexing) can be annotated much more efficiently with the proposed
approach than with current class-domain-wise methods, and produce higher quality
data.",
  address="Springer Verlag",
  booktitle="Advanced Concepts for Intelligent Vision Systems",
  chapter="96955",
  doi="10.1007/978-3-642-33140-4_14",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  number="7517",
  year="2012",
  month="july",
  pages="155--166",
  publisher="Springer Verlag",
  type="conference paper"
}