In praise of artifice reloaded: caution with natural image databases in modeling vision

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Martínez García, Marina
• dc.contributor.author Bertalmío, Marcelo
• dc.contributor.author Malo, Jesús
• dc.date.accessioned 2019-05-22T12:55:31Z
• dc.date.available 2019-05-22T12:55:31Z
• dc.date.issued 2019
• 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).en
• dc.description.sponsorship 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).
• dc.format.mimetype application/pdf
• 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.doi http://dx.doi.org/10.3389/fnins.2019.00008
• dc.identifier.issn 1662-4548
• dc.identifier.uri http://hdl.handle.net/10230/37262
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Neuroscience. 2019 Feb 18;13:8.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/761544
• dc.relation.projectID 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.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Natural stimulien
• dc.subject.keyword Artificial stimulien
• dc.subject.keyword Subjective image quality databasesen
• dc.subject.keyword Wavelet + divisive normalizationen
• dc.subject.keyword Contrast maskingen
• dc.title In praise of artifice reloaded: caution with natural image databases in modeling visionen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion