Header logo is ei

Iterative Kernel Principal Component Analysis for Image Modeling




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.

Author(s): Kim, KI. and Franz, MO. and Schölkopf, B.
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 27
Number (issue): 9
Pages: 1351-1366
Year: 2005
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/TPAMI.2005.181
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Iterative Kernel Principal Component Analysis for Image Modeling},
  author = {Kim, KI. and Franz, MO. and Sch{\"o}lkopf, B.},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {27},
  number = {9},
  pages = {1351-1366},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2005},
  month_numeric = {9}