Optimal contrast based saliency detection
Pattern Recognition Letters
"Mind the gap": tele-registration for structure-driven image completion
ACM Transactions on Graphics (TOG)
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Predicting where we look from spatiotemporal gaps
Proceedings of the 15th ACM on International conference on multimodal interaction
Visual attention computational model using gabor decomposition and 2d entropy
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Selection of a best metric and evaluation of bottom-up visual saliency models
Image and Vision Computing
ACM Transactions on Applied Perception (TAP)
Saliency based mass detection from screening mammograms
Signal Processing
Ensemble dictionary learning for saliency detection
Image and Vision Computing
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Modeling visual attention—particularly stimulus-driven, saliency-based attention—has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.