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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 3/2017

17.02.2017

Iterative Multiplicative Filters for Data Labeling

verfasst von: Ronny Bergmann, Jan Henrik Fitschen, Johannes Persch, Gabriele Steidl

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2017

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Abstract

Based on an idea in Åström et al. (J Math ImagingVis, doi:10.​1007/​s10851-016-0702-4, 2017) we propose a new iterative multiplicative filtering algorithm for label assignment matrices which can be used for the supervised partitioning of data. Starting with a row-normalized matrix containing the averaged distances between prior features and observed ones, the method assigns in a very efficient way labels to the data. We interpret the algorithm as a gradient ascent method with respect to a certain function on the product manifold of positive numbers followed by a reprojection onto a subset of the probability simplex consisting of vectors whose components are bounded away from zero by a small constant. While such boundedness away from zero is necessary to avoid an arithmetic underflow, our convergence results imply that they are also necessary for theoretical reasons. Numerical examples show that the proposed simple and fast algorithm leads to very good results. In particular we apply the method for the partitioning of manifold-valued images.
Vorheriger Artikel Directed Acyclic Graph Continuous Max-Flow Image Segmentation for Unconstrained Label Orderings
Nächster Artikel Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups

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Metadaten
Titel
Iterative Multiplicative Filters for Data Labeling
verfasst von
Ronny Bergmann
Jan Henrik Fitschen
Johannes Persch
Gabriele Steidl
Publikationsdatum
17.02.2017
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 3/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-017-0995-9

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