PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Natural Image Structure with a Horizonal Product Model
Urs Köster, Jussi T. Lindgren, Michael Gutmann and Aapo Hyvärinen
In: ICA 2009, 15-18 Mar 2009, Paraty RJ / Brazil.

Abstract

We present a novel extension to Independent Component Analysis (ICA), where the data is generated as the product of two submodels, each of which follow an ICA model, and which combine in a horizontal fashion. This is in contrast to previous nonlinear extensions to ICA which were based on a hierarchy of layers. We apply the product model to natural image patches and report the emergence of localized masks in the additional network layer, while the Gabor features that are obtained in the primary layer change their tuning properties and become less localized. As an interpretation we suggest that the model learns to separate the localiztion of image features from other properties, since identity and position of a feature are plausibly independent. We also show that the horizontal model can be interpreted as an overcomplete model where the features are no longer indepedent.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4773
Deposited By:Jussi Lindgren
Deposited On:24 March 2009