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) 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.
| Author: | Björn Arlt, Rüdiger W. BrauseGND |
|---|---|
| URN: | urn:nbn:de:hebis:30-79126 |
| Parent Title (German): | Int. Conf. Art. Neural Networks ICANN-98, Skövde, Sweden, 1998 |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2010/09/08 |
| Year of first Publication: | 1998 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2010/09/08 |
| Source: | Int. Conf. Art. Neural Networks ICANN-98, Skövde, Sweden, 1998 |
| HeBIS-PPN: | 227689917 |
| Institutes: | Informatik und Mathematik / Informatik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
