Learning Natural Image Structure with a Horizontal Product Model
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 localization 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 independent.
Conference:
Computational and systems
neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009.
Presentation Type:
Poster Presentation
Topic:
Poster Presentations
Citation:
(2009). Learning Natural Image Structure with a Horizontal Product Model.
Front. Syst. Neurosci.
Conference Abstract:
Computational and systems
neuroscience 2009.
doi: 10.3389/conf.neuro.06.2009.03.012
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Received:
29 Jan 2009;
Published Online:
29 Jan 2009.