About: Learning Vocabularies over a Fine Quantization

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://dx.doi.org/10.1007/s11263-012-0600-1
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. We further demonstrate that the large vocabularies increase the speed of the tf-idf scoring step. 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. We further demonstrate that the large vocabularies increase the speed of the tf-idf scoring step. (en)
Title	Learning Vocabularies over a Fine Quantization Learning Vocabularies over a Fine Quantization (en)
skos:prefLabel	Learning Vocabularies over a Fine Quantization Learning Vocabularies over a Fine Quantization (en)
skos:notation	RIV/68407700:21230/13:00205807!RIV14-GA0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GAP103/12/2310)
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Matas, Jiří Chum, Ondřej Perďoch, Michal Mikulík, Andrej
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	84521
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00205807
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Image retrieval; Vocabulary; Feature track (en)
http://linked.open.../riv/klicoveSlovo	Vocabulary Feature track Image retrieval
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[BE371E55FE8B]
http://linked.open...i/riv/nazevZdroje	International Journal of Computer Vision
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	Large Scale Image and Object Search as a Teacher
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	103
http://linked.open...iv/tvurceVysledku	Matas, Jiří Perďoch, Michal Chum, Ondřej Mikulík, Andrej
http://linked.open...ain/vavai/riv/wos	000318413500007
issn	0920-5691
number of pages	13 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/s11263-012-0600-1
http://localhost/t...ganizacniJednotka	21230

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