Name
Abstract | ||
|---|---|---|
Due to the limited capability for information processing, humans only choose a small amount of input data received from visual field to better understand their environment. The selection of visual input implies the nonuniform distribution of visual attention, which is influenced by environmental visual stimuli and endogenous subject interest. Traditional saliency models do not differentiate individuals, exploring the common trend in attention deployment. This paper investigates individual nuance and association in both saccadic movements and attention distribution, and then discusses how individuality plays a role in predicting attention with low-level and deep features, respectively. It turns out that individual differences indeed exist and can be better discriminated by deep features. In conclusion, individuality not only contributes to improving the accuracy of attention prediction models but also gives us a hint about some interesting viewing behavior that stands out from the crowd pattern. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2800294 | IEEE ACCESS |
Keywords | Field | DocType |
Individuality,visual attention,visual feature | Information processing,Task analysis,Salience (neuroscience),Visualization,Computer science,Cognitive psychology,Feature extraction,Perception,Visual field,Visual perception,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aoqi Li | 1 | 0 | 1.01 |
| Zhenzhong Chen | 2 | 1244 | 101.41 |