Predicting the Perceived Interest of Object in Images

This paper presents the results of a psychophysical experiment and an associated algorithm designed to compute the perceived interest of objects in images. We measured likelihood functions via a psychophysical experiment in which subjects rated the perceived visual interest of each of 408 objects in...

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Bibliographic Details
Published in2008 IEEE Southwest Symposium on Image Analysis and Interpretation pp. 137 - 140
Main Authors Pinneli, S., Chandler, D.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2008
Subjects
Algorithm design and analysis
Bayesian methods
Design engineering
Displays
Image analysis
Image coding
Image databases
Image segmentation
Nonlinear filters
Psychology
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Summary:This paper presents the results of a psychophysical experiment and an associated algorithm designed to compute the perceived interest of objects in images. We measured likelihood functions via a psychophysical experiment in which subjects rated the perceived visual interest of each of 408 objects in 100 images. These results were then used to determine the likelihood of interest given various factors such as size, location, contrast, color, and edge-strength. The resulting likelihood functions are used as part of a Bayesian formulation in which perceived interest is inferred based on these factors. Results demonstrate that our algorithm can perform well in predicting perceived interest.
ISBN:9781424422968
1424422965
DOI:10.1109/SSIAI.2008.4512304