PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Implicit Wiener series for higher-order image analysis
Matthias Franz and Bernhard Schölkopf
In: Neural Information Processing Systems, 13 - 16 Dec 2004, Vancouver, Canada.

Abstract

The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an alternative approach in which multiplicative pixel interactions are described by a series of Wiener functionals. Since the functionals are estimated implicitly via polynomial kernels, the combinatorial explosion associated with the classical higher-order statistics is avoided. First results show that image structures such as lines or corners can be predicted correctly, and that pixel interactions up to the order of five play an important role in natural images.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:375
Deposited By:Matthias Franz
Deposited On:18 December 2004