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...

• Type: Journal
• Year: 2012
• Where: AAAI
• Authors: Ali Borji, Dicky N. Sihite, Laurent Itti
• Keywords: AAAI 2012, Dynamic Bayesian Network, Intelligent Agents, Probabilistic Inference, Probability Distributions