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.

Ciocca, G., Corchs, S., Gasparini, F. (2016). Genetic programming approach to evaluate complexity of texture images. JOURNAL OF ELECTRONIC IMAGING, 25(6) [10.1117/1.JEI.25.6.061408].

Genetic programming approach to evaluate complexity of texture images

CIOCCA, GIANLUIGI
Primo
;
CORCHS, SILVIA ELENA
Secondo
;
GASPARINI, FRANCESCA
Ultimo
2016

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.
Articolo in rivista - Articolo scientifico
genetic programming; image complexity; image features; texture;
Texture, image complexity, Genetic Programming, image features.
English
2016
25
6
061408
reserved
Ciocca, G., Corchs, S., Gasparini, F. (2016). Genetic programming approach to evaluate complexity of texture images. JOURNAL OF ELECTRONIC IMAGING, 25(6) [10.1117/1.JEI.25.6.061408].
File in questo prodotto:
File Dimensione Formato  
Jei.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 3.28 MB
Formato Adobe PDF
3.28 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/127678
Citazioni
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 11
Social impact