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Jacqueline Woolley, Chair The University of Texas at Austin, SEA 4.212, Austin, TX 78712 • (512) 475-7596

"Salience Using Natural Statistics (SUN)"

Mon, October 29, 2012 • 12:00 PM - 1:00 PM • SEA 4.244

The University of Texas at Austin
 
Center for Perceptual Systems Seminar Series
 
 
"Salience Using Natural Statistics (SUN)"
 
Presented by
 
Matthew Tong
 
Ctr for Perceptual Systems, Psych Dpt
The University of Texas at Austin
 
10/29/2012
12:00 PM
SEA 4.244
 
Bag Lunch Talk
 
Find information about current and upcoming talks at CPS on our website:http://www.utexas.edu/cola/centers/cps/events/calendar.php
 
Abstract: One important task of the visual attention system is to focus attentional resources on important objects in a scene. The Salience Using Natural Statistics model (SUN) begins with a
probabilistic definition of this goal and utilizes three kinds of statistical knowledge about the world in choosing which areas of a scene should be attended: what features are rare, the visual
appearance of particular objects of interest, and the locations in a scene likely to contain such objects. These three components emerge naturally from the stated goal and have all been proposed individually before; novelty of features has been argued to attract attention (see
Wolfe, 2001 for a discussion), SUN's appearance model is reminiscent of Guided Search (Wolfe, 1994) and Iconic Search (Rao et al., 1995), and location-based guidance has also been argued to play an important role in directing attention (e.g. Turano, 2003). Unlike other models of salience, SUN learns its statistics in advance from a collection of images of natural scenes to simulate learning natural statistics
through experience. Using the self-information of simple features (their novelty) as a form of task-independent salience results in a model of bottom-up salience for static images and video. SUN's use of natural statistics learned through experience also explains several search asymmetries that challenge some traditional accounts. Of course, bottom-up salience is merely one factor determining where people attend, and SUN's appearance and location components capture some of this task-relevant information to guide attention to important areas in a scene. Using the SUN model, we can therefore gain insight into the roles of bottom-up salience, appearance, location, and context.

Sponsored by: Center for Perceptual Systems Seminar Series


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