Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Automatic Labeling of Colonoscopy Video for Cancer Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
The JAMF Attention Modelling Framework
Attention in Cognitive Systems
Training for Task Specific Keypoint Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Proceedings of the 29th DAGM conference on Pattern recognition
Learning attention based saliency in videos from human eye movements
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
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We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interestingness. We describe the data collection, training, and evaluation process, and show that our learned saliency measure significantly accounts for human eye movements. Furthermore, we illustrate connections to existing interest operators, and present a multi-scale interest point detector based on the learned function.