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Salience Models: A Computational Cognitive Neuroscience Review

Sofia Krasovskaya Orcid Logo, Joe MacInnes Orcid Logo

Vision, Volume: 3, Issue: 4, Start page: 56

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.3390/vision3040056

Abstract

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model—so what made this model so groundbreaking? We have...

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Published in: Vision
ISSN: 2411-5150
Published: MDPI AG 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa63407
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last_indexed 2023-05-31T09:06:05Z
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spelling v2 63407 2023-05-11 Salience Models: A Computational Cognitive Neuroscience Review 06dcb003ec50192bafde2c77bef4fd5c 0000-0002-5134-1601 Joe MacInnes Joe MacInnes true false 2023-05-11 SCS The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model—so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience. Journal Article Vision 3 4 56 MDPI AG 2411-5150 1 12 2019 2019-12-01 10.3390/vision3040056 http://dx.doi.org/10.3390/vision3040056 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This work is supported in part by the HSE academic fund program for the scientific research lab “Vision Modelling Lab”. 2023-06-08T15:09:28.1630048 2023-05-11T11:30:17.0003406 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sofia Krasovskaya 0000-0002-9120-7407 1 Joe MacInnes 0000-0002-5134-1601 2 63407__27648__bf14dc1cc43948d5b338ffd2f79bd211.pdf 63407.pdf 2023-05-31T10:06:00.8368481 Output 750005 application/pdf Version of Record true © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). true eng http://creativecommons.org/licenses/by/4.0/
title Salience Models: A Computational Cognitive Neuroscience Review
spellingShingle Salience Models: A Computational Cognitive Neuroscience Review
Joe MacInnes
title_short Salience Models: A Computational Cognitive Neuroscience Review
title_full Salience Models: A Computational Cognitive Neuroscience Review
title_fullStr Salience Models: A Computational Cognitive Neuroscience Review
title_full_unstemmed Salience Models: A Computational Cognitive Neuroscience Review
title_sort Salience Models: A Computational Cognitive Neuroscience Review
author_id_str_mv 06dcb003ec50192bafde2c77bef4fd5c
author_id_fullname_str_mv 06dcb003ec50192bafde2c77bef4fd5c_***_Joe MacInnes
author Joe MacInnes
author2 Sofia Krasovskaya
Joe MacInnes
format Journal article
container_title Vision
container_volume 3
container_issue 4
container_start_page 56
publishDate 2019
institution Swansea University
issn 2411-5150
doi_str_mv 10.3390/vision3040056
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.3390/vision3040056
document_store_str 1
active_str 0
description The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model—so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
published_date 2019-12-01T15:09:26Z
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score 11.037581

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