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A. Borji, D. N. Sihite, L. Itti, An Object-based Bayesian Framework for Top-down Visual Attention, In: Proc. Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), Toronto, Canada, pp. 1529-1535, Aug 2012. [2012 acceptance rate: 26.0%] (Cited by 18)
Abstract: 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 an- notations of objects in video scenes or by state-of-the-art object detection models. Evaluating over appx. 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 com- pared 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-the-art bottom-up saliency models, and 3) brute-force algo- rithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data.
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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