Biologically-inspired characterization of sparseness in natural images

Laurent U Perrinet (INT)

Acknowledgements:

Jim Bednar @ Continuum Analytics & University of Edinburgh
Wahiba Taouali, Giacomo Benvenuti, Frédéric Chavane
Pascal Wallisch and Tony Movshon @ NYU for the MT data
Rick Adams and Karl Friston @ UCL
Mina Aliakbari Khoei, Guillaume Masson and Anna Montagnini for the psychophysics

EUVIP Special Session on BICV, Wednesday, October 26, 2016

(AUTHOR) Hi, I am Laurent Perrinet from (LOGO) the Institute de Neurosciences de la Timone in Marseille, a joint unit from the CNRS and AMU. Using computational models, I am investigating the link between the efficiency of behavioural responses in vision, their underlying neural code and their adaptation to the structure of the world.
(SHOW TITLE - THEME) = In particular, eye movements, that is, motion of the eyeballs targeted at orienting our gaze in the visual field, provide a large repertoire of behaviors at different time scales from fast, reflexive-like saccades to smooth movememnts and even to long-term adaptation.
(SHOW TITLE -OBJECTIVE) today, I will be focusing on the key challenges in modelizing visually guided eye movements and more generally to better understand decision making in biology (the living kingdom). in particular, we will focus on the dynamics of these mechanisms to unravel the sequence of processes which allow the efficient response of motor commands to the environment. As such, my goal here is to show that the solutions proposed by motion perception and EMs provide a generic (and rather unique) framework to finesse models of decision making in the generic theory championned by Karl Friston, active inference (AI)
(ACKNO) this endeavour involves different techniques, tools and... persons. From the head on, I wish to thank the people who collaborated to this project and in particular Rick Adams and Karl Friston and the Wellcome Trust Centre for Neuroimaging for providing the tools for a successful visit / but also Mina, Guillaume and Anna for the great oppportunity to link theories with psychophysical data; Wahiba, Giacomo and Frédéric for the challenging task of linking such models to the neural activity in NHP, and finally Jean-Bernard, Sohir and Laurent Madelain for their essential knowledge in adaptation and reinforcement.

more precisely... what are eye movements and how can they be useful for active inference?

Outline

Priors in natural images

Quantification of sparseness in natural images

Perspectives: neurophysiology

LP (2015) Sparse models in BICV, http://github.com/bicv/SparseEdges

Methods:

- LogGabor

 in order to do that, we first used a linear transformation using a log-gabor representation

(definition) this representation is a good and generic model of edges as defined by their shape, orientation and scale. It matches what is well described for the response of simple cells' response in area V1. we show here on the top left that these filters tile evenly the Fourier space, but also that these correspond to a good model of edges at different orientation, scale and phase compared to other dictionaries like the Daubechies wavelet base Db4 in (e) and the steerable pyramid by Eero Simoncelli, \item[(Fischer) obviously, this dictionary is over-complete, but their correlation is easy to compute and allow for a relative translation-rotation-scale invariance. we proved that this was better adapted to the extraction of edges than gabors (Fischer, 07).

coupled with a sparse coding algorithm.

- SparseEdges https://laurentperrinet.github.io/sciblog/files/2015-05-22-a-hitchhiker-guide-to-matching-pursuit/MPtutorial_rec.mp4

(MP) from this linear representation, we searched for the most sparse representation using a $\ell_0$ norm approach for which Matching Pursuit proved to be a good approximation. I refer to this paper that appeared in IEEE TIP for more details [LP (2003) IEEE TIP, LP (2015) BICV] (BICV)] and that i reviewed in a recent book chapter (Perrinet, 2015 bicv), including a model of complex cells' response (Fischer, 2007). It is generic and efficient.

We then computed the ``association field'' ... ../ms/figure_model_ls.png}\% - SVM github links

LP (2015) Sparse models in BICV, http://github.com/bicv/SparseEdges

Methods:

- LogGabor

 in order to do that, we first used a linear transformation using a log-gabor representation

(definition) this representation is a good and generic model of edges as defined by their shape, orientation and scale. It matches what is well described for the response of simple cells' response in area V1. we show here on the top left that these filters tile evenly the Fourier space, but also that these correspond to a good model of edges at different orientation, scale and phase compared to other dictionaries like the Daubechies wavelet base Db4 in (e) and the steerable pyramid by Eero Simoncelli, \item[(Fischer) obviously, this dictionary is over-complete, but their correlation is easy to compute and allow for a relative translation-rotation-scale invariance. we proved that this was better adapted to the extraction of edges than gabors (Fischer, 07).

coupled with a sparse coding algorithm.

- SparseEdges https://laurentperrinet.github.io/sciblog/files/2015-05-22-a-hitchhiker-guide-to-matching-pursuit/MPtutorial_rec.mp4

(MP) from this linear representation, we searched for the most sparse representation using a $\ell_0$ norm approach for which Matching Pursuit proved to be a good approximation. I refer to this paper that appeared in IEEE TIP for more details [LP (2003) IEEE TIP, LP (2015) BICV] (BICV)] and that i reviewed in a recent book chapter (Perrinet, 2015 bicv), including a model of complex cells' response (Fischer, 2007). It is generic and efficient.

We then computed the ``association field'' ... ../ms/figure_model_ls.png}\% - SVM github links

LP (2015) Sparse models in BICV, http://github.com/bicv/SparseEdges

Methods:

- LogGabor

 in order to do that, we first used a linear transformation using a log-gabor representation

(definition) this representation is a good and generic model of edges as defined by their shape, orientation and scale. It matches what is well described for the response of simple cells' response in area V1. we show here on the top left that these filters tile evenly the Fourier space, but also that these correspond to a good model of edges at different orientation, scale and phase compared to other dictionaries like the Daubechies wavelet base Db4 in (e) and the steerable pyramid by Eero Simoncelli, \item[(Fischer) obviously, this dictionary is over-complete, but their correlation is easy to compute and allow for a relative translation-rotation-scale invariance. we proved that this was better adapted to the extraction of edges than gabors (Fischer, 07).

coupled with a sparse coding algorithm.

- SparseEdges https://laurentperrinet.github.io/sciblog/files/2015-05-22-a-hitchhiker-guide-to-matching-pursuit/MPtutorial_rec.mp4

(MP) from this linear representation, we searched for the most sparse representation using a $\ell_0$ norm approach for which Matching Pursuit proved to be a good approximation. I refer to this paper that appeared in IEEE TIP for more details [LP (2003) IEEE TIP, LP (2015) BICV] (BICV)] and that i reviewed in a recent book chapter (Perrinet, 2015 bicv), including a model of complex cells' response (Fischer, 2007). It is generic and efficient.

We then computed the ``association field'' ... ../ms/figure_model_ls.png}\% - SVM github links

Outline

Priors in natural images

Quantification of sparseness in natural images

Perspectives: neurophysiology

Outline

Priors in natural images

Quantification of sparseness in natural images

Perspectives: neurophysiology

Biologically-inspired characterization of sparseness in natural images

Laurent U Perrinet (INT)

Acknowledgements:

Jim Bednar @ Continuum Analytics & University of Edinburgh
Wahiba Taouali, Giacomo Benvenuti, Frédéric Chavane
Pascal Wallisch and Tony Movshon @ NYU for the MT data
Rick Adams and Karl Friston @ UCL
Mina Aliakbari Khoei, Guillaume Masson and Anna Montagnini for the psychophysics