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.
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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
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW 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
Tags content based image retrieval, DISA
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/4/2016 21:12.
Abstract
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 projectName: Vizuální vyhledávání obrázků na Webu (Acronym: VisualWeb)
Investor: Czech Science Foundation, Content-based Image Retrieval on the Web Scale
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