Genetic programming approach to evaluate complexity of texture images

Gianluigi Ciocca; Silvia Corchs; Francesca Gasparini

doi:10.1117/1.JEI.25.6.061408

18 July 2016 Genetic programming approach to evaluate complexity of texture images

Gianluigi Ciocca, Silvia Corchs, Francesca Gasparini

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 6, 061408 (July 2016). https://doi.org/10.1117/1.JEI.25.6.061408

Abstract

We adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure MGP exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. MGP outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance.

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Gianluigi Ciocca, Silvia Corchs, and Francesca Gasparini "Genetic programming approach to evaluate complexity of texture images," Journal of Electronic Imaging 25(6), 061408 (18 July 2016). https://doi.org/10.1117/1.JEI.25.6.061408

Published: 18 July 2016

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