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Summary: Representation of images for classification with
independent features
Hervé Le Borgne a*, Anne Guérin-Dugué b, Anestis Antoniadis c
a
Laboratoire des Images et Signaux, Institut National Polytechnique de Grenoble
INPG-LIS, 46 av. Félix Viallet, 38031 Grenoble Cedex, France
b
Communication Langagière et Intéraction Personne Systeme
CLIPS UMR 5524, 385, rue de la Bibliothèque - B.P. 53 - 38041 Grenoble Cedex 9, France
c
Laboratoire de Modélisation et Calcul
IMAG, LMC, BP 53, 38041 Grenoble Cedex 9
Abstract
In this study, Independent Component Analysis (ICA) is used to compute features extracted from natural images.
The use of ICA is justified in the context of classification of natural images for two reasons. On the one hand the
model of image suggests that the underlying statistical principles may be the same as those that determine the
structure of the visual cortex. As a consequence, the filters that ICA produces are adapted to the statistics of natural
images. On the other hand, we adopt a non parametric approach that require density estimation in many dimensions,
and independence between features appears as a solution to overthrow the «curse of dimensionality». Hence we
introduce several signatures of natural images that use these feature, and we define some similarity measures that
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