Inherent Fusion: Towards Scalable Multi-Modal Similarity Search

J 2016

Inherent Fusion: Towards Scalable Multi-Modal Similarity Search

BUDÍKOVÁ, Petra, Michal BATKO, David NOVÁK a Pavel ZEZULA

Základní údaje

Originální název

Inherent Fusion: Towards Scalable Multi-Modal Similarity Search

Autoři

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

Vydání

Journal of Database Management, IGI Global, 2016, 1063-8016

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

Impakt faktor

Impact factor: 0.462

Kód RIV

RIV/00216224:14330/16:00088723

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.4018/JDM.2016100101

UT WoS

000396491200001

Klíčová slova anglicky

Content-Based Retrieval; Evaluation; Image Retrieval; Late Fusion; Multi-Modal Search; Scalability; Similarity Searching

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 4. 2019 09:18, RNDr. Petra Budíková, Ph.D.

Anotace

V originále

The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation.

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, David NOVÁK a Pavel ZEZULA. Inherent Fusion: Towards Scalable Multi-Modal Similarity Search. Journal of Database Management. IGI Global, 2016, roč. 27, č. 4, s. 1-23. ISSN 1063-8016. Dostupné z: https://dx.doi.org/10.4018/JDM.2016100101.

@article{1374148,
   author = {Budíková, Petra and Batko, Michal and Novák, David and Zezula, Pavel},
   article_number = {4},
   doi = {http://dx.doi.org/10.4018/JDM.2016100101},
   keywords = {Content-Based Retrieval; Evaluation; Image Retrieval; Late Fusion; Multi-Modal Search; Scalability; Similarity Searching},
   language = {eng},
   issn = {1063-8016},
   journal = {Journal of Database Management},
   title = {Inherent Fusion: Towards Scalable Multi-Modal Similarity Search},
   volume = {27},
   year = {2016}
}

TY  - JOUR
ID  - 1374148
AU  - Budíková, Petra - Batko, Michal - Novák, David - Zezula, Pavel
PY  - 2016
TI  - Inherent Fusion: Towards Scalable Multi-Modal Similarity Search
JF  - Journal of Database Management
VL  - 27
IS  - 4
SP  - 1-23
EP  - 1-23
PB  - IGI Global
SN  - 10638016
KW  - Content-Based Retrieval
KW  - Evaluation
KW  - Image Retrieval
KW  - Late Fusion
KW  - Multi-Modal Search
KW  - Scalability
KW  - Similarity Searching
N2  - The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation.
ER  -

BUDÍKOVÁ, Petra, Michal BATKO, David NOVÁK a Pavel ZEZULA. Inherent Fusion: Towards Scalable Multi-Modal Similarity Search. \textit{Journal of Database Management}. IGI Global, 2016, roč.~27, č.~4, s.~1-23. ISSN~1063-8016. Dostupné z: https://dx.doi.org/10.4018/JDM.2016100101.

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