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  Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., & Schölkopf, B. (2005). Iterative Kernel Principal Component Analysis for Image Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1351-1366. doi:10.1109/TPAMI.2005.181.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D421-B Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D785-3
Genre: Journal Article

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 Creators:
Kim, KI, Author              
Franz, MO1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

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 Dates: 2005-09
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1109/TPAMI.2005.181
BibTex Citekey: 4938
 Degree: -

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Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  Other : IEEE Trans. Pattern Anal. Mach. Intell.
Source Genre: Journal
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Publ. Info: New York : IEEE Computer Society.
Pages: - Volume / Issue: 27 (9) Sequence Number: - Start / End Page: 1351 - 1366 Identifier: ISSN: 0162-8828
CoNE: https://pure.mpg.de/cone/journals/resource/954925479551