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Erschienen in: Neural Computing and Applications 6/2012

01.09.2012 | LSMS2010 and ICSEE 2010

DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions

verfasst von: Jun Zhang, Guangzhou Zhao, Hong Gu

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

As the feature dimension increases, the original pyramid matching kernel suffers from distortion factors that increase linearly with the feature dimension. This paper proposes a new method by consistently dividing the feature space into two subspaces while generating several levels. In each subspace of the level, the original pyramid matching is used. Then, a weighted sum of every subspace at each level is made as the final measurement of similarity. Experiments on data set Caltech-101 and ETH-80 show that compared with other related algorithms which need hundreds of times of original computation time, dimension partition pyramid matching kernel only needs about 4–6 times less of original computation time to obtain the similar accuracy.

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Metadaten
Titel
DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions
verfasst von
Jun Zhang
Guangzhou Zhao
Hong Gu
Publikationsdatum
01.09.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0953-y

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