Large-scale Image Retrieval using Neural Net Descriptors

D 2015

Large-scale Image Retrieval using Neural Net Descriptors

NOVÁK, David, Michal BATKO and Pavel ZEZULA

Basic information

Original name

Large-scale Image Retrieval using Neural Net Descriptors

Authors

NOVÁK, David (203 Czech Republic, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, guarantor, belonging to the institution)

Edition

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

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

ACM Portal

RIV identification code

RIV/00216224:14330/15:00081693

Organization unit

Faculty of Informatics

ISBN

978-1-4503-3621-5

DOI

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

UT WoS

000382307300158

Keywords in English

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

Abstract

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.

Links

GAP103/10/0886, research and development project

Name: Vizuální vyhledávání obrázků na Webu (Acronym: VisualWeb)

Investor: Czech Science Foundation, Content-based Image Retrieval on the Web Scale

Citovat

NOVÁK, David, Michal BATKO and 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, p. 1039-1040. ISBN 978-1-4503-3621-5. Available from: 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 and 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, p.~1039-1040. ISBN~978-1-4503-3621-5. Available from: https://dx.doi.org/10.1145/2766462.2767868.

Detailed Information on Publication Record