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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F12%3A00057261%21RIV13-GA0-14330___
rdf:type
skos:Concept n7:Vysledek
dcterms:description
Current multimedia search technology is, especially in commercial applications, heavily based on text annotations. However, there are many applications such as image hosting web sites (e.g. Flickr or Picasa) where the text metadata are of poor quality in general. Searching such collections only by text gives usually rather unsatisfactory results. On the other hand, multimedia retrieval systems based purely on content can retrieve visually similar results but lag behind with the ability to grasp the semantics expressed by text annotations. In this paper, we propose various ranking techniques that can be transparently applied on any content-based retrieval system in order to improve the search results quality and user satisfaction. We demonstrate the usefulness of the approach on two large real-life datasets indexed by the MUFIN system. The improvement of the ranked results was evaluated by real users using an online survey. Current multimedia search technology is, especially in commercial applications, heavily based on text annotations. However, there are many applications such as image hosting web sites (e.g. Flickr or Picasa) where the text metadata are of poor quality in general. Searching such collections only by text gives usually rather unsatisfactory results. On the other hand, multimedia retrieval systems based purely on content can retrieve visually similar results but lag behind with the ability to grasp the semantics expressed by text annotations. In this paper, we propose various ranking techniques that can be transparently applied on any content-based retrieval system in order to improve the search results quality and user satisfaction. We demonstrate the usefulness of the approach on two large real-life datasets indexed by the MUFIN system. The improvement of the ranked results was evaluated by real users using an online survey.
dcterms:title
Similarity Query Postprocessing by Ranking Similarity Query Postprocessing by Ranking
skos:prefLabel
Similarity Query Postprocessing by Ranking Similarity Query Postprocessing by Ranking
skos:notation
RIV/00216224:14330/12:00057261!RIV13-GA0-14330___
n7:predkladatel
n12:orjk%3A14330
n5:aktivita
n17:S n17:P
n5:aktivity
P(GA201/09/0683), P(GP201/08/P507), P(VF20102014004), S
n5:dodaniDat
n19:2013
n5:domaciTvurceVysledku
n8:3165647 n8:1235753 n8:8876398
n5:druhVysledku
n13:D
n5:duvernostUdaju
n23:S
n5:entitaPredkladatele
n14:predkladatel
n5:idSjednocenehoVysledku
167880
n5:idVysledku
RIV/00216224:14330/12:00057261
n5:jazykVysledku
n16:eng
n5:klicovaSlova
ranking; content-based retrieval; metric space
n5:klicoveSlovo
n11:content-based%20retrieval n11:metric%20space n11:ranking
n5:kontrolniKodProRIV
[6CEE1AC97949]
n5:mistoKonaniAkce
Linz, Austria
n5:mistoVydani
Berlin
n5:nazevZdroje
Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817
n5:obor
n20:IN
n5:pocetDomacichTvurcuVysledku
3
n5:pocetTvurcuVysledku
3
n5:projekt
n22:GA201%2F09%2F0683 n22:GP201%2F08%2FP507 n22:VF20102014004
n5:rokUplatneniVysledku
n19:2012
n5:tvurceVysledku
Zezula, Pavel Budíková, Petra Batko, Michal
n5:typAkce
n6:WRD
n5:wos
000306440900012
n5:zahajeniAkce
2010-08-17+02:00
s:issn
0302-9743
s:numberOfPages
15
n18:doi
10.1007/978-3-642-27169-4_12
n21:hasPublisher
Springer-Verlag
n15:isbn
9783642271687
n9:organizacniJednotka
14330