dc.contributor.author |
Martínez García, Marina |
dc.contributor.author |
Bertalmío, Marcelo |
dc.contributor.author |
Malo, Jesús |
dc.date |
2019 |
dc.identifier.citation |
Martínez-García M, Bertalmío M, Malo J. In praise of artifice reloaded: caution with natural image databases in modeling vision. Front. Neurosci. 2019 Feb 18;13:8. DOI: 10.3389/fnins.2019.00008 |
dc.identifier.citation |
1662-4548 |
dc.identifier.citation |
http://dx.doi.org/10.3389/fnins.2019.00008 |
dc.identifier.uri |
http://hdl.handle.net/10230/37262 |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
Frontiers |
dc.relation |
Frontiers in Neuroscience. 2019 Feb 18;13:8. |
dc.relation |
info:eu-repo/grantAgreement/EC/H2020/761544 |
dc.relation |
info:eu-repo/grantAgreement/EC/H2020/780470 |
dc.rights |
Copyright © 2019 Martinez-Garcia, Bertalmío and Malo. This is an open-access
article distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with these
terms. |
dc.rights |
https://creativecommons.org/licenses/by/4.0/ |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Natural stimuli |
dc.subject |
Artificial stimuli |
dc.subject |
Subjective image quality databases |
dc.subject |
Wavelet + divisive normalization |
dc.subject |
Contrast masking |
dc.title |
In praise of artifice reloaded: caution with natural image databases in modeling vision |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/publishedVersion |
dc.description.abstract |
Subjective image quality databases are a major source of raw data on how the visual
system works in naturalistic environments. These databases describe the sensitivity of
many observers to a wide range of distortions of different nature and intensity seen
on top of a variety of natural images. Data of this kind seems to open a number of
possibilities for the vision scientist to check the models in realistic scenarios. However,
while these natural databases are great benchmarks for models developed in some other
way (e.g., by using the well-controlled artificial stimuli of traditional psychophysics), they
should be carefully used when trying to fit vision models. Given the high dimensionality
of the image space, it is very likely that some basic phenomena are under-represented
in the database. Therefore, a model fitted on these large-scale natural databases will
not reproduce these under-represented basic phenomena that could otherwise be easily
illustrated with well selected artificial stimuli. In this work we study a specific example of
the above statement. A standard corticalmodel using wavelets and divisive normalization
tuned to reproduce subjective opinion on a large image quality dataset fails to reproduce
basic cross-masking. Here we outline a solution for this problem by using artificial stimuli
and by proposing a modification that makes the model easier to tune. Then, we show
that the modified model is still competitive in the large-scale database. Our simulations
with these artificial stimuli show that when using steerable wavelets, the conventional unit
norm Gaussian kernels in divisive normalization should be multiplied by high-pass filters
to reproduce basic trends in masking. Basic visual phenomena may be misrepresented
in large natural image datasets but this can be solved with model-interpretable stimuli.
This is an additional argument in praise of artifice in line with Rust and Movshon (2005). |
dc.description.abstract |
This work was partially funded by the Spanish and EU FEDER
fund through the MINECO/FEDER/EU grants TIN2015-71537-
P and DPI2017-89867-C2-2-R; and by the European Union’s
Horizon 2020 research and innovation programme under grant
agreement number 761544 (project HDR4EU) and under grant
agreement number 780470 (project SAUCE). |