Large-scale Image Retrieval using Neural Net Descriptors

D 2015

Large-scale Image Retrieval using Neural Net Descriptors

NOVÁK, David, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

Large-scale Image Retrieval using Neural Net Descriptors

Autoři

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

Vydání

New York, NY, USA, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, od s. 1039-1040, 2 s. 2015

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

ACM Portal

Kód RIV

RIV/00216224:14330/15:00081693

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-3621-5

DOI

http://dx.doi.org/10.1145/2766462.2767868

UT WoS

000382307300158

Klíčová slova anglicky

metric indexing; deep convolutional neural network; contentbased image retrieval; k-NN search

Štítky

content based image retrieval, DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2016 21:12, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

One of current big challenges in computer science is development of data management and retrieval techniques that would keep pace with the evolution of contemporary data and with the growing expectations on data processing. Various digital images became a common part of both public and enterprise data collections and there is a natural requirement that the retrieval should consider more the actual visual content of the image data. In our demonstration, we aim at the task of retrieving images that are visually and semantically similar to a given example image; the system should be able to online evaluate k nearest neighbor queries within a collection containing tens of millions of images. The applicability of such a system would be, for instance, on stock photography sites, in e-shops searching in product photos, or in collections from a constrained Web image search.

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, Michal BATKO a Pavel ZEZULA. Large-scale Image Retrieval using Neural Net Descriptors. Online. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2015, s. 1039-1040. ISBN 978-1-4503-3621-5. Dostupné z: https://dx.doi.org/10.1145/2766462.2767868.

@inproceedings{1341690,
   author = {Novák, David and Batko, Michal and Zezula, Pavel},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval},
   doi = {http://dx.doi.org/10.1145/2766462.2767868},
   keywords = {metric indexing; deep convolutional neural network; contentbased image retrieval; k-NN search},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-3621-5},
   pages = {1039-1040},
   publisher = {ACM},
   title = {Large-scale Image Retrieval using Neural Net Descriptors},
   url = {http://dl.acm.org/citation.cfm?doid=2766462.2767868},
   year = {2015}
}

TY  - JOUR
ID  - 1341690
AU  - Novák, David - Batko, Michal - Zezula, Pavel
PY  - 2015
TI  - Large-scale Image Retrieval using Neural Net Descriptors
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450336215
KW  - metric indexing
KW  - deep convolutional neural network
KW  - contentbased image retrieval
KW  - k-NN search
UR  - http://dl.acm.org/citation.cfm?doid=2766462.2767868
L2  - http://dl.acm.org/citation.cfm?doid=2766462.2767868
N2  - One of current big challenges in computer science is development of data management and retrieval techniques that would keep pace with the evolution of contemporary data and with the growing expectations on data processing. Various digital images became a common part of both public and enterprise data collections and there is a natural requirement that the retrieval should consider more the actual visual content of the image data. In our demonstration, we aim at the task of retrieving images that are visually and semantically similar to a given example image; the system should be able to online evaluate k nearest neighbor queries within a collection containing tens of millions of images. The applicability of such a system would be, for instance, on stock photography sites, in e-shops searching in product photos, or in collections from a constrained Web image search.
ER  -

NOVÁK, David, Michal BATKO a Pavel ZEZULA. Large-scale Image Retrieval using Neural Net Descriptors. Online. In \textit{Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval}. New York, NY, USA: ACM, 2015, s.~1039-1040. ISBN~978-1-4503-3621-5. Dostupné z: https://dx.doi.org/10.1145/2766462.2767868.

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