ConceptRank for search-based image annotation

J 2018

ConceptRank for search-based image annotation

BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

ConceptRank for search-based image annotation

Autoři

BUDÍKOVÁ, Petra (203 Česká republika, domácí), Michal BATKO (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.101

Kód RIV

RIV/00216224:14330/18:00100721

Organizační jednotka

Fakulta informatiky

DOI

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

UT WoS

000429355800048

Klíčová slova anglicky

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

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 20. 4. 2022 11:26, RNDr. Petra Budíková, Ph.D.

Anotace

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.

Návaznosti

GBP103/12/G084, projekt VaV

Název: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Grantová agentura ČR, Centrum pro multi-modální interpretaci dat velkého rozsahu

Citovat

BUDÍKOVÁ, Petra, Michal BATKO a Pavel ZEZULA. ConceptRank for search-based image annotation. Multimedia Tools and Applications. Kluwer Academic Publishers, 2018, roč. 77, č. 7, s. 8847-8882. ISSN 1380-7501. Dostupné z: 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 a Pavel ZEZULA. ConceptRank for search-based image annotation. \textit{Multimedia Tools and Applications}. Kluwer Academic Publishers, 2018, roč.~77, č.~7, s.~8847-8882. ISSN~1380-7501. Dostupné z: https://dx.doi.org/10.1007/s11042-017-4777-8.

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