Visual search is· a ubiquitous task in everyday life. Simple tasks, from finding cars keys to reaching for a door handle, all require an element of visual search. In some critical situations, such as in security surveillance or for Search and Rescue situations, there is an advantage in augmenting the human search capability with computer assistance. The aim of this thesis is to investigate different computational models of human visual perception in the context of natural image search .. Using two psychophysical experiments to investigate human visual search behaviour, this thesis demonstrates similarities and differences between search tasks using completely natural scenes and the previous literature of artificially constructed search scenes. The human experimental results are compared to the results from several computational models of scene content that use the analysis of either the clutter content of a scene, salience of objects in the scene, or the relevance to the target of locations in a scene. Additionally a new model of visual attention is developed, using the principle of a relevancy heat map to identify regions of a scene that match a specific target. The results suggest that the type of model that best predicts visual search behaviour depends upon the nature of the search task. Models that account for the details of the target through a principle of relevance are better for matching target-present behaviour and that models that use simple saliency of the, features better predict fixations in target-absent searches.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:653081 |
| Date | January 2013 |
| Creators | Asher, Matthew F. |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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