The principal independent components of images

This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) ha
This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the neural network community. Applied to images, we aim for the most important source patterns with the highest occurrence probability or highest information called principal independent components (PIC). For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. 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.
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Metadaten
Author:Björn Arlt, Rüdiger W. Brause
URN:urn:nbn:de:hebis:30-79126
Document Type:Article
Language:English
Date of Publication (online):2010/09/08
Year of first Publication:1998
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/09/08
Source:Int. Conf. Art. Neural Networks ICANN-98, Skövde, Sweden, 1998
HeBIS PPN:227689917
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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