Learning Vocabularies over a Fine Quantization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00205807" target="_blank" >RIV/68407700:21230/13:00205807 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s11263-012-0600-1" target="_blank" >http://dx.doi.org/10.1007/s11263-012-0600-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11263-012-0600-1" target="_blank" >10.1007/s11263-012-0600-1</a>
Alternative languages
Result language
angličtina
Original language name
Learning Vocabularies over a Fine Quantization
Original language description
A novel similarity measure for bag-of-words type large scale image retrieval is presented. The similarity function is learned in an unsupervised manner, requires no extra space over the standard bag-of-words method and is more discriminative than both L2-based soft assignment and Hamming embedding. The novel similarity function achieves mean average precision that is superior to any result published in the literature on the standard Oxford 5k, Oxford 105k and Paris datasets/protocols. We study the effect of a fine quantization and very large vocabularies (up to 64 million words) and show that the performance of specific object retrieval increases with the size of the vocabulary. This observation is in contradiction with previously published methods. Wefurther demonstrate that the large vocabularies increase the speed of the tf-idf scoring step.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP103%2F12%2F2310" target="_blank" >GAP103/12/2310: Large Scale Image and Object Search as a Teacher</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computer Vision
ISSN
0920-5691
e-ISSN
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Volume of the periodical
103
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
163-175
UT code for WoS article
000318413500007
EID of the result in the Scopus database
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