Iterative kernel principal component analysis for image modeling

Kim, Kwang In and Franz, Matthias O. and Schölkopf, Bernhard (2005) Iterative kernel principal component analysis for image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (9). pp. 1351-1366. ISSN 0162-8828

Full text not available from this repository.


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 and denoising performance are comparable to existing methods.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
03 Jul 2014 10:10
Last Modified:
22 Nov 2022 00:56