Cargando…
An improved saliency model of visual attention dependent on image content
Many visual attention models have been presented to obtain the saliency of a scene, i.e., the visually significant parts of a scene. However, some mechanisms are still not taken into account in these models, and the models do not fit the human data accurately. These mechanisms include which visual f...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011177/ https://www.ncbi.nlm.nih.gov/pubmed/36926377 http://dx.doi.org/10.3389/fnhum.2022.862588 |
_version_ | 1784906332155936768 |
---|---|
author | Novin, Shabnam Fallah, Ali Rashidi, Saeid Daliri, Mohammad Reza |
author_facet | Novin, Shabnam Fallah, Ali Rashidi, Saeid Daliri, Mohammad Reza |
author_sort | Novin, Shabnam |
collection | PubMed |
description | Many visual attention models have been presented to obtain the saliency of a scene, i.e., the visually significant parts of a scene. However, some mechanisms are still not taken into account in these models, and the models do not fit the human data accurately. These mechanisms include which visual features are informative enough to be incorporated into the model, how the conspicuity of different features and scales of an image may integrate to obtain the saliency map of the image, and how the structure of an image affects the strategy of our attention system. We integrate such mechanisms in the presented model more efficiently compared to previous models. First, besides low-level features commonly employed in state-of-the-art models, we also apply medium-level features as the combination of orientations and colors based on the visual system behavior. Second, we use a variable number of center-surround difference maps instead of the fixed number used in the other models, suggesting that human visual attention operates differently for diverse images with different structures. Third, we integrate the information of different scales and different features based on their weighted sum, defining the weights according to each component's contribution, and presenting both the local and global saliency of the image. To test the model's performance in fitting human data, we compared it to other models using the CAT2000 dataset and the Area Under Curve (AUC) metric. Our results show that the model has high performance compared to the other models (AUC = 0.79 and sAUC = 0.58) and suggest that the proposed mechanisms can be applied to the existing models to improve them. |
format | Online Article Text |
id | pubmed-10011177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100111772023-03-15 An improved saliency model of visual attention dependent on image content Novin, Shabnam Fallah, Ali Rashidi, Saeid Daliri, Mohammad Reza Front Hum Neurosci Human Neuroscience Many visual attention models have been presented to obtain the saliency of a scene, i.e., the visually significant parts of a scene. However, some mechanisms are still not taken into account in these models, and the models do not fit the human data accurately. These mechanisms include which visual features are informative enough to be incorporated into the model, how the conspicuity of different features and scales of an image may integrate to obtain the saliency map of the image, and how the structure of an image affects the strategy of our attention system. We integrate such mechanisms in the presented model more efficiently compared to previous models. First, besides low-level features commonly employed in state-of-the-art models, we also apply medium-level features as the combination of orientations and colors based on the visual system behavior. Second, we use a variable number of center-surround difference maps instead of the fixed number used in the other models, suggesting that human visual attention operates differently for diverse images with different structures. Third, we integrate the information of different scales and different features based on their weighted sum, defining the weights according to each component's contribution, and presenting both the local and global saliency of the image. To test the model's performance in fitting human data, we compared it to other models using the CAT2000 dataset and the Area Under Curve (AUC) metric. Our results show that the model has high performance compared to the other models (AUC = 0.79 and sAUC = 0.58) and suggest that the proposed mechanisms can be applied to the existing models to improve them. Frontiers Media S.A. 2023-02-28 /pmc/articles/PMC10011177/ /pubmed/36926377 http://dx.doi.org/10.3389/fnhum.2022.862588 Text en Copyright © 2023 Novin, Fallah, Rashidi and Daliri. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Human Neuroscience Novin, Shabnam Fallah, Ali Rashidi, Saeid Daliri, Mohammad Reza An improved saliency model of visual attention dependent on image content |
title | An improved saliency model of visual attention dependent on image content |
title_full | An improved saliency model of visual attention dependent on image content |
title_fullStr | An improved saliency model of visual attention dependent on image content |
title_full_unstemmed | An improved saliency model of visual attention dependent on image content |
title_short | An improved saliency model of visual attention dependent on image content |
title_sort | improved saliency model of visual attention dependent on image content |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011177/ https://www.ncbi.nlm.nih.gov/pubmed/36926377 http://dx.doi.org/10.3389/fnhum.2022.862588 |
work_keys_str_mv | AT novinshabnam animprovedsaliencymodelofvisualattentiondependentonimagecontent AT fallahali animprovedsaliencymodelofvisualattentiondependentonimagecontent AT rashidisaeid animprovedsaliencymodelofvisualattentiondependentonimagecontent AT dalirimohammadreza animprovedsaliencymodelofvisualattentiondependentonimagecontent AT novinshabnam improvedsaliencymodelofvisualattentiondependentonimagecontent AT fallahali improvedsaliencymodelofvisualattentiondependentonimagecontent AT rashidisaeid improvedsaliencymodelofvisualattentiondependentonimagecontent AT dalirimohammadreza improvedsaliencymodelofvisualattentiondependentonimagecontent |