The principal independent components of images

Classically, encoding of images by only a few, important components is done by the Principal Component Analysis (PCA). Recently, a data analysis tool called Independent Component Analysis (ICA) for the separation of inde
Classically, encoding of images by only a few, important components is done by the Principal Component Analysis (PCA). Recently, a data analysis tool called Independent Component Analysis (ICA) for the separation of independent influences in signals has found strong interest in the neural network community. This approach has also been applied to images. Whereas the approach assumes continuous source channels mixed up to the same number of channels by a mixing matrix, we assume that images are composed by only a few image primitives. This means that for images we have less sources than pixels. Additionally, in order to reduce unimportant information, we aim only for the most important source patterns with the highest occurrence probabilities or biggest information called „Principal Independent Components (PIC)“. For the example of a synthetic picture composed by characters this idea gives us the most important ones. Nevertheless, for natural images where no a-priori probabilities can be computed this does not lead to an acceptable reproduction error. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
show moreshow less

Download full text files

Export metadata

  • Export Bibtex
  • Export RIS

Additional Services

    Share in Twitter Search Google Scholar
Metadaten
Author:Björn Arlt, Rüdiger W. Brause
URN:urn:nbn:de:hebis:30-67810
Parent Title (German):Universität Frankfurt am Main. Fachbereich Informatik: Interner Bericht ; 98,1
Series (Serial Number):Interner Bericht / Fachbereich Informatik, Johann Wolfgang Goethe-Universität Frankfurt a.M. (98, 01)
Document Type:Working Paper
Language:English
Year of Completion:1998
Year of first Publication:1998
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2009/07/14
Tag:Independent Component Analysis ICA ; Principal Component Analysis PCA ; Principal Independent Component Analysis PICA ; Rate Distortion Theory
SWD-Keyword:Bildverarbeitung; Hauptkomponentenanalyse; Unabhängige Komponentenanalyse
HeBIS PPN:215695143
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

$Rev: 11761 $