Abstract
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance evaluation metrics, there is no clear consensus on which evaluation metric is the best. This work proposes a subjective study that uses ratings from human observers to evaluate saliency maps computed by existing VA models based on comparing the maps visually with ground-truth maps obtained from eye-tracking data. The subjective ratings are correlated with the scores obtained from existing as well as a proposed objective VA performance evaluation metric using several correlation measures. The correlation results show that the proposed objective VA metric outperforms the existing metrics.
Original language | English (US) |
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Title of host publication | 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 2792-2796 |
Number of pages | 5 |
Volume | 2016-August |
ISBN (Electronic) | 9781467399616 |
DOIs | |
State | Published - Aug 3 2016 |
Event | 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016 |
Other
Other | 23rd IEEE International Conference on Image Processing, ICIP 2016 |
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Country/Territory | United States |
City | Phoenix |
Period | 9/25/16 → 9/28/16 |
Keywords
- Subjective Study
- VA Models
- VA Performance Metrics
- Visual Attention
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing