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2012

An Object-Based Bayesian Framework for Top-Down Visual Attention

6 years 9 months ago
An Object-Based Bayesian Framework for Top-Down Visual Attention
We introduce a new task-independent framework to model top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a Dynamic Bayesian Network (DBN) that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions which are fed from manual annotations of objects in video scenes or by state-of-theart object detection models. Evaluating over ∼3 hours (appx. 315,000 eye fixations and 12,600 saccades) of observers playing 3 video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multi-modal information from gist, bottom-up saliency, physical actions, and events) to eye positions, 2) 14 state-of-theart bottom-up saliency models, an...
Ali Borji, Dicky N. Sihite, Laurent Itti
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Ali Borji, Dicky N. Sihite, Laurent Itti
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