A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
An attentional approach for perceptual grouping of spatially distributed patterns
Proceedings of the 29th DAGM conference on Pattern recognition
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Model performance for visual attention in real 3d color scenes
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention
IEEE Transactions on Image Processing
Towards standardization of metrics for evaluation of artificial visual attention
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
| Hi-index | 0.00 |
Every field of science requires standardization of metrics and measurement methods for detecting true advancement in research. Efforts on computational models of visual attention models have increased in the recent years and now it is important to have standard measuring techniques in this area in order to avoid undue deceleration in its progress. This paper performs a review of the evaluation techniques used by different researchers in the field and brings them in an organized structure. Further methods and metrics are also proposed that would lead to more objective and quantitative evaluation of the attention models.