Journal article 370 views 59 downloads
Salience Models: A Computational Cognitive Neuroscience Review
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Joe MacInnes
Vision, Volume: 3, Issue: 4, Start page: 56
Swansea University Author:
Joe MacInnes
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© 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/).
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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...
| Published in: | Vision |
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| ISSN: | 2411-5150 |
| Published: |
MDPI AG
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63407 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
This work is supported in part by the HSE academic fund program for the scientific research lab “Vision Modelling Lab”. |
| Issue: |
4 |
| Start Page: |
56 |