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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00192658%21RIV13-GA0-21230___
rdf:type
n13:Vysledek skos:Concept
dcterms:description
In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs. In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.
dcterms:title
Rotation-Invariant Image and Video Description With Local Binary Pattern Features Rotation-Invariant Image and Video Description With Local Binary Pattern Features
skos:prefLabel
Rotation-Invariant Image and Video Description With Local Binary Pattern Features Rotation-Invariant Image and Video Description With Local Binary Pattern Features
skos:notation
RIV/68407700:21230/12:00192658!RIV13-GA0-21230___
n13:predkladatel
n19:orjk%3A21230
n4:aktivita
n15:P
n4:aktivity
P(GAP103/10/1585)
n4:cisloPeriodika
4
n4:dodaniDat
n11:2013
n4:domaciTvurceVysledku
n9:1711326
n4:druhVysledku
n17:J
n4:duvernostUdaju
n18:S
n4:entitaPredkladatele
n12:predkladatel
n4:idSjednocenehoVysledku
166207
n4:idVysledku
RIV/68407700:21230/12:00192658
n4:jazykVysledku
n14:eng
n4:klicovaSlova
texture classification; Fourier transform; dynamic texture; local binary patterns (LBP); rotation invariance
n4:klicoveSlovo
n5:rotation%20invariance n5:dynamic%20texture n5:texture%20classification n5:Fourier%20transform n5:local%20binary%20patterns%20%28LBP%29
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[CE20387F4C32]
n4:nazevZdroje
IEEE Transactions on Image Processing
n4:obor
n10:JD
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
4
n4:projekt
n16:GAP103%2F10%2F1585
n4:rokUplatneniVysledku
n11:2012
n4:svazekPeriodika
21
n4:tvurceVysledku
Zhao, G. Ahonen, T. Matas, Jiří Pietikäinen, M.
n4:wos
000302181800004
s:issn
1057-7149
s:numberOfPages
13
n6:doi
10.1109/TIP.2011.2175739
n20:organizacniJednotka
21230