An Evaluation of Saliency and Its Limits
| dc.contributor.advisor | Tsotsos, John K. | |
| dc.contributor.author | Wloka, Calden Frank | |
| dc.date.accessioned | 2019-11-22T18:56:02Z | |
| dc.date.available | 2019-11-22T18:56:02Z | |
| dc.date.copyright | 2019-07 | |
| dc.date.issued | 2019-11-22 | |
| dc.date.updated | 2019-11-22T18:56:02Z | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Doctoral | |
| dc.degree.name | PhD - Doctor of Philosophy | |
| dc.description.abstract | The field of computational saliency modelling has its origins in psychophysical studies of visual search and low-level attention, but over the years has heavily shifted focus to performance-based model development and benchmarking. This dissertation examines the current state of saliency research from the perspective of its relationship to human visual attention, and presents research along three different but complementary avenues: a critical examination of the metrics used to measure saliency model performance, a software library intended to facilitate the exploration of saliency model applications outside of standard benchmarks, and a novel model of fixation control that extends fixation prediction beyond a static saliency map to an explicit prediction of an ordered sequence of saccades. The examination of metrics provides a more direct window into algorithm spatial bias than competing methods, as well as presents evidence that spatial considerations cannot be completely isolated from stimulus appearance when accounting for human fixation locations. Experimentation over psychophysical stimuli reveals that many of the most recent models, all which achieve high benchmark performance for fixation prediction, fail to identify salient targets in basic feature search, more complex singleton search, and search asymmetries, suggesting an overemphasis on the specific performance benchmarks that are widely used in saliency modelling research and a need for more diverse evaluation. Further experiments are performed to test how different saliency algorithms predict fixations across space and time, finding a consistent spatiotemporal pattern of saliency prediction across almost all tested algorithms. The fixation control model outperforms competing methods at saccade sequence prediction according to a number of trajectory-based metrics, and produces qualitatively more human-like fixation traces than those sampled from static maps. The results of these studies together suggest that the role of saliency should not be viewed in isolation, but rather as a component of a larger visual attention system, and this work provides a number of tools and techniques that will facilitate further understanding of visual attention. | |
| dc.identifier.uri | http://hdl.handle.net/10315/36782 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject | Psychology | |
| dc.subject.keywords | Computer vision | |
| dc.subject.keywords | Saliency | |
| dc.subject.keywords | Attention | |
| dc.subject.keywords | Eye movements | |
| dc.title | An Evaluation of Saliency and Its Limits | |
| dc.type | Electronic Thesis or Dissertation |
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