Content-based annotation and classification framework: a
general multi-purpose approach

D 2013

Content-based annotation and classification framework: a general multi-purpose approach

BATKO, Michal, Jan BOTOREK, Petra BUDÍKOVÁ and Pavel ZEZULA

Basic information

Original name

Content-based annotation and classification framework: a general multi-purpose approach

Authors

BATKO, Michal (203 Czech Republic, belonging to the institution), Jan BOTOREK (203 Czech Republic, belonging to the institution), Petra BUDÍKOVÁ (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

New York, NY, USA, Proceedings of the 17th International Database Engineering & Applications Symposium, p. 58-67, 10 pp. 2013

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

storage medium (CD, DVD, flash disk)

RIV identification code

RIV/00216224:14330/13:00065734

Organization unit

Faculty of Informatics

ISBN

978-1-4503-2025-2

DOI

http://dx.doi.org/10.1145/2513591.2513651

Keywords in English

Automatic image annotation; classication; content-based search; hierarchical approach

Abstract

V originále

Unprecedented amounts of digital data are becoming available nowadays, but frequently the data lack some semantic information necessary to effectively organize these resources. For images in particular, textual annotations that represent the semantics are highly desirable. Only a small percentage of images is created with reliable annotations, therefore a lot of effort is being invested into automatic image annotation. In this paper, we address the annotation problem from a general perspective and introduce a new annotation model that is applicable to many text assignment problems. We also provide experimental results from several implemented instances of our model.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

VF20102014004, research and development project

Name: Multimediální analýza (Acronym: Multimediální analýza)

Investor: Ministry of the Interior of the CR

Citovat

BATKO, Michal, Jan BOTOREK, Petra BUDÍKOVÁ and Pavel ZEZULA. Content-based annotation and classification framework: a general multi-purpose approach. In Proceedings of the 17th International Database Engineering & Applications Symposium. New York, NY, USA: ACM, 2013, p. 58-67. ISBN 978-1-4503-2025-2. Available from: https://dx.doi.org/10.1145/2513591.2513651.

@inproceedings{1127649,
   author = {Batko, Michal and Botorek, Jan and Budíková, Petra and Zezula, Pavel},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 17th International Database Engineering & Applications Symposium},
   doi = {http://dx.doi.org/10.1145/2513591.2513651},
   keywords = {Automatic image annotation; classication; content-based search; hierarchical approach},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-2025-2},
   pages = {58-67},
   publisher = {ACM},
   title = {Content-based annotation and classification framework: a general multi-purpose approach},
   year = {2013}
}

TY  - JOUR
ID  - 1127649
AU  - Batko, Michal - Botorek, Jan - Budíková, Petra - Zezula, Pavel
PY  - 2013
TI  - Content-based annotation and classification framework: a general multi-purpose approach
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450320252
KW  - Automatic image annotation
KW  - classication
KW  - content-based search
KW  - hierarchical approach
N2  - Unprecedented amounts of digital data are becoming available nowadays, but frequently the data lack some semantic information necessary to effectively organize these resources. For images in particular, textual annotations that represent the semantics are highly desirable. Only a small percentage of images is created with reliable annotations, therefore a lot of effort is being invested into automatic image annotation. In this paper, we address the annotation problem from a general perspective and introduce a new annotation model that is applicable to many text assignment problems. We also provide experimental results from several implemented instances of our model.
ER  -

BATKO, Michal, Jan BOTOREK, Petra BUDÍKOVÁ and Pavel ZEZULA. Content-based annotation and classification framework: a general multi-purpose approach. In \textit{Proceedings of the 17th International Database Engineering \&{} Applications Symposium}. New York, NY, USA: ACM, 2013, p.~58-67. ISBN~978-1-4503-2025-2. Available from: https://dx.doi.org/10.1145/2513591.2513651.

Detailed Information on Publication Record