Special Section on Color in Texture and Material Recognition

Genetic programming approach to evaluate complexity of texture images

[+] Author Affiliations
Gianluigi Ciocca, Silvia Corchs, Francesca Gasparini

University of Milano-Bicocca, Dipartimento di Informatica, Sistemistica e Comunicazione, Viale Sarca 336, 20126 Milano, Italy

NeuroMi—Milan Center for Neuroscience, Milan, Italy

J. Electron. Imaging. 25(6), 061408 (Jul 18, 2016). doi:10.1117/1.JEI.25.6.061408
History: Received February 8, 2016; Accepted June 27, 2016
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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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© 2016 SPIE and IS&T

Citation

Gianluigi Ciocca ; Silvia Corchs and Francesca Gasparini
"Genetic programming approach to evaluate complexity of texture images", J. Electron. Imaging. 25(6), 061408 (Jul 18, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.061408


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