Document Detail

Efficient bubbles for visual categorization tasks.
MedLine Citation:
PMID:  21524660     Owner:  NLM     Status:  Publisher    
Bubbles is a classification image technique that randomly samples visual information from input stimuli to derive the diagnostic features that observers use in visual categorization tasks. To reach statistical significance, bubbles performs an exhaustive and repetitive search in the stimulus space. To reduce the search trials, we developed an adaptive method that uses reinforcement learning techniques to optimize sampling by exploiting the observer's history of categorization. We compared the performance of the original and the adaptive bubbles algorithms in a model observer and eight human adults who all resolved the same visual categorization task (i.e., five facial expressions of emotion). We demonstrate the feasibility of a substantial reduction (by a factor of ∼2) in the number of search trials required to locate the same diagnostic features with the adaptive method, but only when the observer reaches a performance threshold of 50% correct for each expression category. When this threshold is not reached, both the original and adaptive algorithms converge in the same number of trials.
Hong Fang Wang; Nial Friel; Frederic Gosselin; Philippe G Schyns
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-16
Journal Detail:
Title:  Vision research     Volume:  -     ISSN:  1878-5646     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0417402     Medline TA:  Vision Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011. Published by Elsevier Ltd.
Institute of Neuroscience and Psychology, University of Glasgow, UK.
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