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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F12%3A10123411%21RIV13-GA0-11320___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches - the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval. The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches - the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval.
dcterms:title
Visual Image Search: Feature Signatures or/and Global Descriptors Visual Image Search: Feature Signatures or/and Global Descriptors
skos:prefLabel
Visual Image Search: Feature Signatures or/and Global Descriptors Visual Image Search: Feature Signatures or/and Global Descriptors
skos:notation
RIV/00216208:11320/12:10123411!RIV13-GA0-11320___
n7:predkladatel
n8:orjk%3A11320
n3:aktivita
n18:P
n3:aktivity
P(GAP202/11/0968), P(GPP202/12/P297)
n3:cisloPeriodika
7404
n3:dodaniDat
n4:2013
n3:domaciTvurceVysledku
n6:3885364 n6:5851726
n3:druhVysledku
n20:J
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
177563
n3:idVysledku
RIV/00216208:11320/12:10123411
n3:jazykVysledku
n19:eng
n3:klicovaSlova
Feature Signatures; MPEG-7 Descriptors; SQFD; CBIR
n3:klicoveSlovo
n12:CBIR n12:SQFD n12:Feature%20Signatures n12:MPEG-7%20Descriptors
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[545478F2690B]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n11:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:projekt
n16:GPP202%2F12%2FP297 n16:GAP202%2F11%2F0968
n3:rokUplatneniVysledku
n4:2012
n3:svazekPeriodika
2012
n3:tvurceVysledku
Michal, Batko David, Novák Skopal, Tomáš Lokoč, Jakub
s:issn
0302-9743
s:numberOfPages
15
n5:doi
10.1007/978-3-642-32153-5_13
n17:organizacniJednotka
11320