Biologically-inspired characterization of sparseness in natural images

Abstract : Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. We designed a sparse coding algorithm biologically-inspired by the architecture of the primary visual cortex. We show here that coefficients of this representation exhibit a heavy-tailed distribution. For each image, the parameters of this distribution characterize sparseness and vary from image to image. To investigate the role of this sparseness, we designed a new class of random textured stimuli with a controlled sparseness value inspired by our measurements on natural images. Then, we provide with a method to synthesize random textures images with a given statistics for sparseness that matches that of some given class of natural images and provide perspectives for their use in neurophysiology.
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Communication dans un congrès
6th European Workshop on Visual Information Processing (EUVIP), Oct 2016, Marseille, France. pp.1--6, 2016, <http://ieeexplore.ieee.org/document/7764592/>. <10.1109/EUVIP.2016.7764592>
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https://hal-amu.archives-ouvertes.fr/hal-01461404
Contributeur : Laurent Perrinet <>
Soumis le : mercredi 8 février 2017 - 16:56:06
Dernière modification le : lundi 13 février 2017 - 09:04:06

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Laurent U Perrinet. Biologically-inspired characterization of sparseness in natural images. 6th European Workshop on Visual Information Processing (EUVIP), Oct 2016, Marseille, France. pp.1--6, 2016, <http://ieeexplore.ieee.org/document/7764592/>. <10.1109/EUVIP.2016.7764592>. <hal-01461404>

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