Podrobný výpis o publikaci

NOVÁK, David. Multi-modal Similarity Retrieval with Distributed Key-value Store. Online. 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. [citováno 2024-04-24]

Další formáty: BibTeX LaTeX RIS

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
Originální 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
Změnil	Změnil: RNDr. David Novák, Ph.D., učo 4335. Změněno: 6. 4. 2016 14:13.

Anotace
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.

Anotace

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)
GAP103/10/0886, projekt VaV	Investor: Grantová agentura ČR, Vizuální vyhledávání obrázků na Webu