Empirical Validation of the Saliency-based Model of Visual Attention
Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Computer Vision Center Press
2003-12-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.es/article/view/66 |
| _version_ | 1818724765108535296 |
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| author | Nabil Ouerhani Roman von Wartburg Heinz Hugli Rene Muri |
| author_facet | Nabil Ouerhani Roman von Wartburg Heinz Hugli Rene Muri |
| author_sort | Nabil Ouerhani |
| collection | DOAJ |
| description | Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliency-based model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention - the saliency map - produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model. |
| first_indexed | 2024-12-17T21:31:37Z |
| format | Article |
| id | doaj.art-737fcf1510a7481fafa62978d943a42b |
| institution | Directory Open Access Journal |
| issn | 1577-5097 |
| language | English |
| last_indexed | 2024-12-17T21:31:37Z |
| publishDate | 2003-12-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj.art-737fcf1510a7481fafa62978d943a42b2022-12-21T21:31:51ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972003-12-013110.5565/rev/elcvia.6638Empirical Validation of the Saliency-based Model of Visual AttentionNabil OuerhaniRoman von WartburgHeinz HugliRene MuriVisual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliency-based model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention - the saliency map - produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model.https://elcvia.cvc.uab.es/article/view/66preattentive visionVisual attentionBio-inspired vision algorithms |
| spellingShingle | Nabil Ouerhani Roman von Wartburg Heinz Hugli Rene Muri Empirical Validation of the Saliency-based Model of Visual Attention ELCVIA Electronic Letters on Computer Vision and Image Analysis preattentive vision Visual attention Bio-inspired vision algorithms |
| title | Empirical Validation of the Saliency-based Model of Visual Attention |
| title_full | Empirical Validation of the Saliency-based Model of Visual Attention |
| title_fullStr | Empirical Validation of the Saliency-based Model of Visual Attention |
| title_full_unstemmed | Empirical Validation of the Saliency-based Model of Visual Attention |
| title_short | Empirical Validation of the Saliency-based Model of Visual Attention |
| title_sort | empirical validation of the saliency based model of visual attention |
| topic | preattentive vision Visual attention Bio-inspired vision algorithms |
| url | https://elcvia.cvc.uab.es/article/view/66 |
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