Efficient bubbles for visual categorization tasks.
Vision Res
; 51(12): 1318-23, 2011 Jun 21.
Article
in En
| MEDLINE
| ID: mdl-21524660
ABSTRACT
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 â¼2) 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Visual Perception
/
Software
/
Recognition, Psychology
/
Emotions
/
Facial Expression
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Vision Res
Year:
2011
Type:
Article
Affiliation country:
United kingdom