Open AccessA scale invariant measure of clutter
Author(s) -
Mary J. Bravo,
Hany Farid
Publication year - 2008
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/8.1.23
Subject(s) - clutter , segmentation , measure (data warehouse) , artificial intelligence , mathematics , pattern recognition (psychology) , scale (ratio) , scale invariance , exponent , invariant (physics) , data set , power law , computer vision , computer science , statistics , radar , physics , data mining , telecommunications , linguistics , philosophy , mathematical physics , quantum mechanics
We propose a measure of clutter for real images that can be used to predict search times. This measure uses an efficient segmentation algorithm (P. Felzenszwalb & D. Huttenlocher, 2004) to count the number of regions in an image. This number is not uniquely defined, however, because it varies with the scale of segmentation. The relationship between the number of regions and the scale of segmentation follows a power law, and the exponent of the power law is similar across images. We fit power law functions to the multiple scale segmentations of 160 images. The power law exponent was set to the average value for the set of images, and the constant of proportionality was used as a measure of image clutter. The same 160 images were also used as stimuli in a visual search experiment. This scale-invariant measure of clutter accounted for about 40% of the variance in the visual search times.