PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Implicit Wiener series for capturing higher-order interactions in images. Sensory coding and the natural environment
Matthias Franz and Bernhard Schölkopf
In: Sensory coding and the natural environment, 6 - 10 Sep 2004, Oxford, UK.

Abstract

The information about the objects in an image is almost exclusively described by the higher-order interactions of its pixels. The Wiener series is one of the standard methods to systematically characterize these interactions. However, the classical estimation method of the Wiener expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear signals such as images. We propose an estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems using polynomial kernels as known from Support Vector Machines and other kernel-based methods. Numerical experiments show performance advantages in terms of convergence, interpretability and system sizes that can be handled. By the time of the conference, we will be able to present first results on the higher-order structure of natural images.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:380
Deposited By:Matthias Franz
Deposited On:18 December 2004