Multi-modal Similarity Retrieval with Distributed Key-value
Store

J 2015

Multi-modal Similarity Retrieval with Distributed Key-value Store

NOVÁK, David

Základní údaje

Originální název

Multi-modal Similarity Retrieval with Distributed Key-value Store

Autoři

NOVÁK, David (203 Česká republika, garant, domácí)

Vydání

MOBILE NETWORKS & APPLICATIONS, DORDRECHT, SPRINGER, 2015, 1383-469X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 1.538

Kód RIV

RIV/00216224:14330/15:00081691

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1007/s11036-014-0561-4

UT WoS

000360003900013

Klíčová slova anglicky

Similarity search; Multi-modal search; Big Data; Scalability; Distributed hash table

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 6. 4. 2016 14:13, RNDr. David Novák, Ph.D.

Anotace

V originále

We propose a system architecture for large-scale similarity search in various types of digital data. The architecture combines contemporary highly-scalable distributed data stores with recent efficient similarity indexes and also with other types of search indexes. The system enables various types of data access by distance-based similarity queries, standard term and attribute queries, and advanced queries combining several search aspects (modalities). The first part of this work describes the generic architecture and similarity index PPP-Codes, which is suitable for our system. In the second part, we describe two specific instances of this architecture that manage two large collections of digital images and provide content-based visual search, keyword search, attribute-based access, and their combinations. The first collection is the CoPhIR benchmark with 106 million images accessed by MPEG7 visual descriptors and the second collection contains 20 million images with complex features obtained from deep convolutional neural network.

Návaznosti

GAP103/10/0886, projekt VaV

Název: Vizuální vyhledávání obrázků na Webu (Akronym: VisualWeb)

Investor: Grantová agentura ČR, Vizuální vyhledávání obrázků na Webu

Citovat

NOVÁK, David. Multi-modal Similarity Retrieval with Distributed Key-value Store. MOBILE NETWORKS & APPLICATIONS. DORDRECHT: SPRINGER, 2015, roč. 20, č. 4, s. 521-532. ISSN 1383-469X. Dostupné z: https://dx.doi.org/10.1007/s11036-014-0561-4.

@article{1341681,
   author = {Novák, David},
   article_location = {DORDRECHT},
   article_number = {4},
   doi = {http://dx.doi.org/10.1007/s11036-014-0561-4},
   keywords = {Similarity search; Multi-modal search; Big Data; Scalability; Distributed hash table},
   language = {eng},
   issn = {1383-469X},
   journal = {MOBILE NETWORKS & APPLICATIONS},
   title = {Multi-modal Similarity Retrieval with Distributed Key-value Store},
   volume = {20},
   year = {2015}
}

TY  - JOUR
ID  - 1341681
AU  - Novák, David
PY  - 2015
TI  - Multi-modal Similarity Retrieval with Distributed Key-value Store
JF  - MOBILE NETWORKS & APPLICATIONS
VL  - 20
IS  - 4
SP  - 521-532
EP  - 521-532
PB  - SPRINGER
SN  - 1383469X
KW  - Similarity search
KW  - Multi-modal search
KW  - Big Data
KW  - Scalability
KW  - Distributed hash table
N2  - We propose a system architecture for large-scale similarity search in various types of digital data. The architecture combines contemporary highly-scalable distributed data stores with recent efficient similarity indexes and also with other types of search indexes. The system enables various types of data access by distance-based similarity queries, standard term and attribute queries, and advanced queries combining several search aspects (modalities). The first part of this work describes the generic architecture and similarity index PPP-Codes, which is suitable for our system. In the second part, we describe two specific instances of this architecture that manage two large collections of digital images and provide content-based visual search, keyword search, attribute-based access, and their combinations. The first collection is the CoPhIR benchmark with 106 million images accessed by MPEG7 visual descriptors and the second collection contains 20 million images with complex features obtained from deep convolutional neural network.
ER  -

NOVÁK, David. Multi-modal Similarity Retrieval with Distributed Key-value Store. \textit{MOBILE NETWORKS \&{}amp; APPLICATIONS}. DORDRECHT: SPRINGER, 2015, roč.~20, č.~4, s.~521-532. ISSN~1383-469X. Dostupné z: https://dx.doi.org/10.1007/s11036-014-0561-4.

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