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L. Itti, P. F. Baldi, A Surprising Theory of Attention, Hughes Research Laboratories, Malibu, California, Sep 2004.
Abstract: The concept of information is central to science, technology, and biology. Shannon's information theory, although successful for developing computer and telecommunication technologies, does not capture subjective and semantic aspects of information not related to its transmission but rather to expectations of observers. We propose a subjective definition of information we call surprise, to quantify how data affects a natural or artificial observer, by measuring the difference between prior and posterior beliefs of that observer. We argue that surprise is better suited to studying subjective aspects of brain function and behavior, particularly sensory processing and novelty detection. Thus, we build a computational model of early vision and attention, which topographically computes visual surprise. It outperforms Shannon information and other models in predicting gaze of four humans watching 50 complex videoclips. The resulting surprise theory of attention and subjective information foraging is applicable across different modalities, datatypes, tasks, and abstraction levels.
Themes: Computational Modeling, Model of Bottom-Up Saliency-Based Visual Attention, Computer Vision, Bayesian Theory of Surprise, Human Eye-Tracking Research
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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