Paper Info
Title
Reverse engineering the visual system via genetic programs
Abstract
We propose a datamining based method for automated reverse engineering of search strategies during active visual search tasks. The method uses a genetic program (GP) that evolves populations of fuzzy decision trees and selects an optimal one. Previous psychophysical observations of subjects engaged in a simple search task result in a database of stimulus conditions and concomitant measures of eye gaze information and associated psychophysical metrics that globally describe the subjects search strategies. Fuzzy rules about the likely design properties of the components of the visual system involved in selecting fixation location during search are defined based on these metrics. A fitness function that incorporates both the fuzzy rules and the information in the database is used to conduct GP based datamining. The information extracted through the GP process is the internal design specification of the visual system vis-à-vis active visual search.
Year
DOI
Venue
2007
10.1007/978-3-540-73216-7_22
HCI (16)
Keywords
Field
DocType
active visual search,fuzzy decision tree,search strategy,subjects search strategy,genetic program,simple search task result,active visual search task,gp process,visual system,fuzzy rule,internal design specification,fitness function,fuzzy logic,eye gaze,eye tracking,reverse engineering,information extraction,visual search,knowledge discovery
Visual search,Computer science,Reverse engineering,Fuzzy logic,Genetic programming,Fitness function,Eye tracking,Artificial intelligence,Knowledge extraction,Genetic program,Machine learning
Conference
Volume
ISSN
Citations 
4565
0302-9743
0
PageRank 
References 
Authors
0.34
2
1
Name
Order
Citations
PageRank
D. A. Simoni152.01