Comparing computational models of vision to human behaviour
Biological vision and computational models of vision can be split into three independent components (image description, decision process, and image set). The thesis presented here aimed to investigate the influence of each of these core components on computational model’s similarity to human behavio...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2018
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| Online Access: | https://eprints.nottingham.ac.uk/50196/ |
| _version_ | 1848798181679169536 |
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| author | Colvin, Thomas |
| author_facet | Colvin, Thomas |
| author_sort | Colvin, Thomas |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Biological vision and computational models of vision can be split into three independent components (image description, decision process, and image set). The thesis presented here aimed to investigate the influence of each of these core components on computational model’s similarity to human behaviour. Chapter 3 investigated the similarity of different computational image descriptors to their biological counterparts, using an image matching task. The results showed that several of the computational models could explain a significant amount of the variance in human performance on individual images. The deep supervised convolutional neural net explained the most variance, followed by GIST, HMAX and then PHOW. Chapter 4 investigated which computational decision process best explained observers’ behaviour on an image categorization task. The results showed that Decision Bound theory produced behaviour the closest to that of observers. This was followed by Exemplar theory and Prototype theory. Chapter 5 examined whether the naturally differing image set between computational models and observers could partially account for the difference in their behaviour. The results showed that, indeed, the naturally differing image set between computational models and observers was affecting the similarity of their behaviour. This gap did not alter which image descriptor best fit observers’ behaviour and could be reduced by training observers on the image set the computational models were using. Chapter 6 investigated, using computational models of vision, the impact of the neighbouring (masking) images on the target images in a RSVP task. This was done by combining the neighbouring images with the target image for the computational models’ simulation for each trial. The results showed that models behaviour became closer to that of the human observers when the neighbouring mask images were included in the computational simulations, as would be expected given an integration period for neural mechanisms.
This thesis has shown that computational models can show quite similar behaviours to human observers, even at the level of how they perform with individual images. While this shows the potential utility in computational models as a tool to study visual processing, It has also shown the need to take into account many aspects of the overall model of the visual process and task; not only the image description, but the task requirements, the decision processes, the images being used as stimuli and even the sequence in which they are presented. |
| first_indexed | 2025-11-14T20:15:42Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-50196 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:15:42Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-501962025-02-28T12:02:58Z https://eprints.nottingham.ac.uk/50196/ Comparing computational models of vision to human behaviour Colvin, Thomas Biological vision and computational models of vision can be split into three independent components (image description, decision process, and image set). The thesis presented here aimed to investigate the influence of each of these core components on computational model’s similarity to human behaviour. Chapter 3 investigated the similarity of different computational image descriptors to their biological counterparts, using an image matching task. The results showed that several of the computational models could explain a significant amount of the variance in human performance on individual images. The deep supervised convolutional neural net explained the most variance, followed by GIST, HMAX and then PHOW. Chapter 4 investigated which computational decision process best explained observers’ behaviour on an image categorization task. The results showed that Decision Bound theory produced behaviour the closest to that of observers. This was followed by Exemplar theory and Prototype theory. Chapter 5 examined whether the naturally differing image set between computational models and observers could partially account for the difference in their behaviour. The results showed that, indeed, the naturally differing image set between computational models and observers was affecting the similarity of their behaviour. This gap did not alter which image descriptor best fit observers’ behaviour and could be reduced by training observers on the image set the computational models were using. Chapter 6 investigated, using computational models of vision, the impact of the neighbouring (masking) images on the target images in a RSVP task. This was done by combining the neighbouring images with the target image for the computational models’ simulation for each trial. The results showed that models behaviour became closer to that of the human observers when the neighbouring mask images were included in the computational simulations, as would be expected given an integration period for neural mechanisms. This thesis has shown that computational models can show quite similar behaviours to human observers, even at the level of how they perform with individual images. While this shows the potential utility in computational models as a tool to study visual processing, It has also shown the need to take into account many aspects of the overall model of the visual process and task; not only the image description, but the task requirements, the decision processes, the images being used as stimuli and even the sequence in which they are presented. 2018-07-19 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/50196/1/Thesis%20Main%2018.03.05.pdf Colvin, Thomas (2018) Comparing computational models of vision to human behaviour. PhD thesis, University of Nottingham. Computer vision Computational model image recognition categorization decision training neural networks |
| spellingShingle | Computer vision Computational model image recognition categorization decision training neural networks Colvin, Thomas Comparing computational models of vision to human behaviour |
| title | Comparing computational models of vision to human behaviour |
| title_full | Comparing computational models of vision to human behaviour |
| title_fullStr | Comparing computational models of vision to human behaviour |
| title_full_unstemmed | Comparing computational models of vision to human behaviour |
| title_short | Comparing computational models of vision to human behaviour |
| title_sort | comparing computational models of vision to human behaviour |
| topic | Computer vision Computational model image recognition categorization decision training neural networks |
| url | https://eprints.nottingham.ac.uk/50196/ |