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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F09%3A00029662%21RIV10-GA0-14330___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
The Content-based Photo Image Retrieval (CoPhIR) dataset is the largest available database of digital images with corresponding visual descriptors. It contains five MPEG-7 global descriptors extracted from more than 106 million images from Flickr photo-sharing system. In this paper, we analyze this dataset focusing on 1) efficiency of similarity-based indexing and searching and on 2) expressiveness of combination of the descriptors with respect to subjective perception of visual similarity. We treat the descriptors as metric spaces and then combine them into a multi-metric space. We analyze distance distributions of individual descriptors, measure intrinsic dimensionality of these datasets and statistically evaluate correlation between these descriptors. Further, we use two methods to assess subjective accuracy and satisfaction of similarity retrieval based on a combination of descriptors that is recommended for CoPhIR, and we compare these results on databases of 10 and 100 million CoPhIR images. The Content-based Photo Image Retrieval (CoPhIR) dataset is the largest available database of digital images with corresponding visual descriptors. It contains five MPEG-7 global descriptors extracted from more than 106 million images from Flickr photo-sharing system. In this paper, we analyze this dataset focusing on 1) efficiency of similarity-based indexing and searching and on 2) expressiveness of combination of the descriptors with respect to subjective perception of visual similarity. We treat the descriptors as metric spaces and then combine them into a multi-metric space. We analyze distance distributions of individual descriptors, measure intrinsic dimensionality of these datasets and statistically evaluate correlation between these descriptors. Further, we use two methods to assess subjective accuracy and satisfaction of similarity retrieval based on a combination of descriptors that is recommended for CoPhIR, and we compare these results on databases of 10 and 100 million CoPhIR images.
dcterms:title
CoPhIR Image Collection under the Microscope CoPhIR Image Collection under the Microscope
skos:prefLabel
CoPhIR Image Collection under the Microscope CoPhIR Image Collection under the Microscope
skos:notation
RIV/00216224:14330/09:00029662!RIV10-GA0-14330___
n4:aktivita
n5:P
n4:aktivity
P(GA201/09/0683)
n4:dodaniDat
n8:2010
n4:domaciTvurceVysledku
n14:3445771 n14:1235753 n14:8876398
n4:druhVysledku
n16:D
n4:duvernostUdaju
n9:S
n4:entitaPredkladatele
n6:predkladatel
n4:idSjednocenehoVysledku
308339
n4:idVysledku
RIV/00216224:14330/09:00029662
n4:jazykVysledku
n20:eng
n4:klicovaSlova
metric space; MPEG-7; visual descriptors; CoPhIR dataset; dataset analysis
n4:klicoveSlovo
n11:dataset%20analysis n11:MPEG-7 n11:visual%20descriptors n11:CoPhIR%20dataset n11:metric%20space
n4:kontrolniKodProRIV
[353FDADB7C6F]
n4:mistoKonaniAkce
Prague, Czech Republic
n4:mistoVydani
Washington, DC, USA
n4:nazevZdroje
Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
n4:obor
n13:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n19:GA201%2F09%2F0683
n4:rokUplatneniVysledku
n8:2009
n4:tvurceVysledku
Novák, David Budíková, Petra Batko, Michal
n4:typAkce
n17:WRD
n4:zahajeniAkce
2009-01-01+01:00
s:numberOfPages
8
n15:hasPublisher
IEEE Computer Society
n3:isbn
978-0-7695-3765-8
n7:organizacniJednotka
14330