ConceptRank for search-based image annotation

J 2018

ConceptRank for search-based image annotation

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA

Basic information

Original name

ConceptRank for search-based image annotation

Authors

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

Edition

Multimedia Tools and Applications, Kluwer Academic Publishers, 2018, 1380-7501

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.101

RIV identification code

RIV/00216224:14330/18:00100721

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1007/s11042-017-4777-8

UT WoS

000429355800048

Keywords in English

Search-based image annotation; Content-based image retrieval; kNN classification; Biased random walk with restarts; Semantic analysis; ConceptRank

Abstract

V originále

Multimedia information is becoming an ubiquitous part of our lives, which brings an equally ubiquitous need for efficient multimedia retrieval. One of the possible solutions to this problem is to attach text descriptions to multimedia data objects, thus allowing users to utilize traditional text search mechanisms. Search-based annotation techniques attempt to determine the descriptive keywords by analyzing the descriptions of similar, already annotated multimedia objects, which are detected by content-based retrieval techniques. One of the main challenges of this approach is the extraction of semantically connected keywords from the possibly noisy descriptions of similar objects. In this paper, we address this challenge by proposing the ConceptRank, a new keyword ranking algorithm that exploits semantic relationships between candidate keywords and utilizes the random walk mechanism to compute the probability of individual candidates. The effectiveness of the ConceptRank algorithm is evaluated in context of web image annotation. We present a complex image annotation system that includes the ConceptRank component, and compare it to other state-of-the–art annotation techniques.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

Citovat

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. ConceptRank for search-based image annotation. Multimedia Tools and Applications. Kluwer Academic Publishers, 2018, vol. 77, No 7, p. 8847-8882. ISSN 1380-7501. Available from: https://dx.doi.org/10.1007/s11042-017-4777-8.

@article{1390179,
   author = {Budíková, Petra and Batko, Michal and Zezula, Pavel},
   article_number = {7},
   doi = {http://dx.doi.org/10.1007/s11042-017-4777-8},
   keywords = {Search-based image annotation; Content-based image retrieval; kNN classification; Biased random walk with restarts; Semantic analysis; ConceptRank},
   language = {eng},
   issn = {1380-7501},
   journal = {Multimedia Tools and Applications},
   title = {ConceptRank for search-based image annotation},
   url = {https://link.springer.com/article/10.1007/s11042-017-4777-8},
   volume = {77},
   year = {2018}
}

TY  - JOUR
ID  - 1390179
AU  - Budíková, Petra - Batko, Michal - Zezula, Pavel
PY  - 2018
TI  - ConceptRank for search-based image annotation
JF  - Multimedia Tools and Applications
VL  - 77
IS  - 7
SP  - 8847-8882
EP  - 8847-8882
PB  - Kluwer Academic Publishers
SN  - 13807501
KW  - Search-based image annotation
KW  - Content-based image retrieval
KW  - kNN classification
KW  - Biased random walk with restarts
KW  - Semantic analysis
KW  - ConceptRank
UR  - https://link.springer.com/article/10.1007/s11042-017-4777-8
N2  - Multimedia information is becoming an ubiquitous part of our lives, which brings an equally ubiquitous need for efficient multimedia retrieval. One of the possible solutions to this problem is to attach text descriptions to multimedia data objects, thus allowing users to utilize traditional text search mechanisms. Search-based annotation techniques attempt to determine the descriptive keywords by analyzing the descriptions of similar, already annotated multimedia objects, which are detected by content-based retrieval techniques. One of the main challenges of this approach is the extraction of semantically connected keywords from the possibly noisy descriptions of similar objects. In this paper, we address this challenge by proposing the ConceptRank, a new keyword ranking algorithm that exploits semantic relationships between candidate keywords and utilizes the random walk mechanism to compute the probability of individual candidates. The effectiveness of the ConceptRank algorithm is evaluated in context of web image annotation. We present a complex image annotation system that includes the ConceptRank component, and compare it to other state-of-the–art annotation techniques.
ER  -

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. ConceptRank for search-based image annotation. \textit{Multimedia Tools and Applications}. Kluwer Academic Publishers, 2018, vol.~77, No~7, p.~8847-8882. ISSN~1380-7501. Available from: https://dx.doi.org/10.1007/s11042-017-4777-8.

Detailed Information on Publication Record